5. Post-Modeling.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    " <h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\"  role=\"tab\" aria-controls=\"home\">Notebook Content</h3>\n",
    "<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\"  role=\"tab\" aria-controls=\"home\">Evaluation Metrics for Regression</h3>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Explained-Variance-Score\" role=\"tab\" aria-controls=\"profile\">Explained Variance Score<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Max-error\" role=\"tab\" aria-controls=\"profile\">Max error<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Mean-Absolute-Error\" role=\"tab\" aria-controls=\"profile\">Mean Absolute Error<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Mean-Squared-Error\" role=\"tab\" aria-controls=\"profile\">Mean Squared Error<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Mean-Squared-Logarithmic-Error\" role=\"tab\" aria-controls=\"profile\">Mean Squared Logarithmic Error<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Median-Absolute-Error\" role=\"tab\" aria-controls=\"profile\">Median Absolute Error<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#R²-score\" role=\"tab\" aria-controls=\"profile\">R² score<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "    <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Mean-Percentage-Error\" role=\"tab\" aria-controls=\"profile\">Mean Percentage Error<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "    <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Mean-Absolute-Percentage-Error\" role=\"tab\" aria-controls=\"profile\">Mean Absolute Percentage Error<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "    <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Weighted-Mean-Absolute-error\" role=\"tab\" aria-controls=\"profile\">Weighted Mean Absolute Percentage Error<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "    <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Tips-for-Metric-Selection\" role=\"tab\" aria-controls=\"profile\">Tips for Metric Selection<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Cross-Validation\" role=\"tab\" aria-controls=\"messages\">Cross Validation<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#StratifiedKFold\" role=\"tab\" aria-controls=\"messages\">StratifiedKFold<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\"  role=\"tab\" aria-controls=\"home\">Accuracy Metrics for Binary Classification</h3>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Cohen’s-kappa \" role=\"tab\" aria-controls=\"profile\">Cohen’s kappa <span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Hamming-Loss\" role=\"tab\" aria-controls=\"profile\">Hamming Loss<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Confusion-Matrix\" role=\"tab\" aria-controls=\"messages\">Confusion Matrix<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Precision-Recall-(PR)-Curve\" role=\"tab\" aria-controls=\"messages\">Precision-Recall (PR) Curve<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#ROC-(Receiver-Operating-Characteristics)\" role=\"tab\" aria-controls=\"messages\">ROC (Receiver Operating Characteristics)<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#AUC-(Area-Under-the-Curve)\" role=\"tab\" aria-controls=\"messages\">AUC (Area Under the Curve)<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "  <h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\"  role=\"tab\" aria-controls=\"home\">Accuracy Metrics for Multi-Class Classification</h3>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Accuarcy-Score\" role=\"tab\" aria-controls=\"messages\">Accuarcy Score<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Confusion-Matrix-for-MultiClass\" role=\"tab\" aria-controls=\"messages\">Confusion Matrix for MultiClass<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#ROC-and-AUC\" role=\"tab\" aria-controls=\"messages\">ROC and AUC<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Precision-Recall-Curve-for-Multiclass\" role=\"tab\" aria-controls=\"messages\">Precision Recall Curve for Multiclass<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Classification-Report\" role=\"tab\" aria-controls=\"messages\">Classification Report<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation Metrics for Regression\n",
    "The **sklearn.metrics module implements several loss, score, and utility functions to measure regression performance**. Some of those have been enhanced to handle the **multioutput case**: mean_squared_error, mean_absolute_error, explained_variance_score and r2_score.These functions have an **multioutput keyword argument which specifies the way the scores or losses for each individual target should be averaged**. The default is 'uniform_average', which specifies a uniformly weighted mean over outputs.<br> \n",
    "If an ndarray of shape (n_outputs,) is passed, then its entries are interpreted as weights and an according weighted average is returned. If multioutput is 'raw_values' is specified, then all unaltered individual scores or losses will be returned in an array of shape (n_outputs,).\n",
    "<br><br>The **r2_score and explained_variance_score accept an additional value 'variance_weighted'** for the multioutput parameter. This option leads to a weighting of each individual score by the variance of the corresponding target variable. This setting quantifies the globally captured unscaled variance. If the target variables are of different scale, then this score puts more importance on well explaining the higher variance variables. multioutput='variance_weighted' is the default value for r2_score for backward compatibility. This will be changed to uniform_average in the future.<br><br>\n",
    "\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Code\" role=\"tab\" aria-controls=\"messages\">Go to Code<span class=\"badge badge-primary badge-pill\"> </span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explained Variance Score\n",
    "The explained_variance_score **computes the explained variance regression score**. **Explained variation measures the proportion to which a mathematical model accounts for the variation (dispersion)** of a given data set. Often, variation is quantified as variance; then, the more specific term explained variance can be used.<br><br>The best possible score is 1.0, lower values are worse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If $\\hat{y}$ is the estimated target output, $y$ the corresponding (correct) target output, and $Var$ is Variance, the square of the standard deviation, then the explained variance is estimated as follow:\n",
    "\n",
    "$explained\\_{}variance(y, \\hat{y}) = 1 - \\frac{Var\\{ y - \\hat{y}\\}}{Var\\{y\\}}$<br><br>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Code\" role=\"tab\" aria-controls=\"messages\">Go to Code<span class=\"badge badge-primary badge-pill\"> </span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Max error\n",
    "The max_error function computes the **maximum residual error** , a metric that captures the worst case error between the predicted value and the true value. In a perfectly fitted single output regression model, max_error would be 0 on the training set and though this would be highly unlikely in the real world, this metric shows the extent of error that the model had when it was fitted.\n",
    "\n",
    "If $\\hat{y}_i$ is the predicted value of the $i$-th sample, and $y_i$ is the corresponding true value, then the max error is defined as:\n",
    "\n",
    "$\\text{Max Error}(y, \\hat{y}) = max(| y_i - \\hat{y}_i |)$<br><br>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Code\" role=\"tab\" aria-controls=\"messages\">Go to Code<span class=\"badge badge-primary badge-pill\"> </span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Absolute Error\n",
    "The mean_absolute_error function computes mean absolute error, a risk metric corresponding to the expected value of the absolute error loss or $l1$-norm loss.In statistics, mean absolute error (MAE) is a **measure of errors between paired observations** expressing the same phenomenon.\n",
    "\n",
    "If $\\hat{y}_i$ is the predicted value of the $i$-th sample, and $y_i$ is the corresponding true value, then the mean absolute error (MAE) estimated over $n_{\\text{samples}}$ is defined as:\n",
    "\n",
    "$\\text{MAE}(y, \\hat{y}) = \\frac{1}{n_{\\text{samples}}} \\sum_{i=0}^{n_{\\text{samples}}-1} \\left| y_i - \\hat{y}_i \\right|$.<br><br>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Code\" role=\"tab\" aria-controls=\"messages\">Go to Code<span class=\"badge badge-primary badge-pill\"> </span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Squared Error\n",
    "The mean_squared_error function computes mean square error, a risk metric **corresponding to the expected value of the squared (quadratic) error or loss**.\n",
    "\n",
    "If $\\hat{y}_i$ is the predicted value of the $i$-th sample, and $y_i$ is the corresponding true value, then the mean squared error (MSE) estimated over $n_{\\text{samples}}$ is defined as\n",
    "\n",
    "$\\text{MSE}(y, \\hat{y}) = \\frac{1}{n_\\text{samples}} \\sum_{i=0}^{n_\\text{samples} - 1} (y_i - \\hat{y}_i)^2.$<br><br>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Code\" role=\"tab\" aria-controls=\"messages\">Go to Code<span class=\"badge badge-primary badge-pill\"> </span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Squared Logarithmic Error\n",
    "The mean_squared_log_error function computes a **risk metric corresponding to the expected value of the squared logarithmic (quadratic) error or loss.**\n",
    "\n",
    "If $\\hat{y}_i$ is the predicted value of the $i$-th sample, and $y_i$ is the corresponding true value, then the mean squared logarithmic error (MSLE) estimated over $n_{\\text{samples}}$ is defined as:\n",
    "\n",
    "$\\text{MSLE}(y, \\hat{y}) = \\frac{1}{n_\\text{samples}} \\sum_{i=0}^{n_\\text{samples} - 1} (\\log_e (1 + y_i) - \\log_e (1 + \\hat{y}_i) )^2.$\n",
    "Where $\\log_e (x)$ means the natural logarithm of $x$. <br>\n",
    "**This metric is best to use when targets having exponential growth, such as population counts**, average sales of a commodity over a span of years etc. Note that this metric penalizes an under-predicted estimate greater than an over-predicted estimate.\n",
    "<br><br><a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Code\" role=\"tab\" aria-controls=\"messages\">Go to Code<span class=\"badge badge-primary badge-pill\"> </span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Median Absolute Error\n",
    "The median_absolute_error is particularly interesting because **it is robust to outliers**. The loss is calculated by taking the **median of all absolute differences between the target and the prediction**.<br><br>\n",
    "If $\\hat{y}_i$ is the predicted value of the $i$-th sample and $y_i$ is the corresponding true value, then the median absolute error (MedAE) estimated over $n_{\\text{samples}}$ is defined as\n",
    "\n",
    "$\\text{MedAE}(y, \\hat{y}) = \\text{median}(\\mid y_1 - \\hat{y}_1 \\mid, \\ldots, \\mid y_n - \\hat{y}_n \\mid).$\n",
    "\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Code\" role=\"tab\" aria-controls=\"messages\">Go to Code<span class=\"badge badge-primary badge-pill\"> </span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R² score\n",
    "The r2_score function **computes the coefficient of determination**, usually denoted as R².<br><br>\n",
    "It represents the **proportion of variance (of y) that has been explained by the independent variables** in the model. It provides an **indication of goodness of fit** and therefore a measure of how well unseen samples are likely to be predicted by the model, through the proportion of explained variance.<br><br>\n",
    "As such variance is dataset dependent, **R² may not be meaningfully comparable across different datasets**. <br>Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R² score of 0.0.\n",
    "\n",
    "If $\\hat{y}_i$ is the predicted value of the $i$-th sample and $y_i$ is the corresponding true value for total $n$ samples, the estimated R² is defined as:\n",
    "\n",
    "$R^2(y, \\hat{y}) = 1 - \\frac{\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2}{\\sum_{i=1}^{n} (y_i - \\bar{y})^2}$ <br>\n",
    "where $\\bar{y} = \\frac{1}{n} \\sum_{i=1}^{n} y_i$ and $\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2 = \\sum_{i=1}^{n} \\epsilon_i^2$ <br>\n",
    "\n",
    "\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Code\" role=\"tab\" aria-controls=\"messages\">Go to Code<span class=\"badge badge-primary badge-pill\"> </span></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
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<title id="MathJax-SVG-1-Title">{\displaystyle {\text{MPE}}={\frac {100\%}{n}}\sum _{t=1}^{n}{\frac {a_{t}-f_{t}}{a_{t}}}}</title>
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Percentage Error\n",
    "In statistics, the mean percentage error (MPE) is the computed average of percentage errors by which forecasts of a model differ from actual values of the quantity being forecast.\n",
    "\n",
    "The formula for the mean percentage error is:\n",
    "![MPE.svg](attachment:MPE.svg) <br>\n",
    "where $a_t$ is the actual value of the quantity being forecast, $f_t$ is the forecast, and n is the number of different times for which the variable is forecast.\n",
    "\n",
    "Because actual rather than absolute values of the forecast errors are used in the formula, positive and negative forecast errors can offset each other; as a result the formula can be used as a measure of the bias in the forecasts.\n",
    "\n",
    "A disadvantage of this measure is that it is undefined whenever a single actual value is zero.<br>\n",
    "The fuction defination of MPE is given below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MPE(y_true, y_pred): \n",
    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "    return np.mean((y_true - y_pred) / y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "attachments": {
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<title id="MathJax-SVG-1-Title">{\displaystyle {\mbox{M}}={\frac {1}{n}}\sum _{t=1}^{n}\left|{\frac {A_{t}-F_{t}}{A_{t}}}\right|,}</title>
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Absolute Percentage Error\n",
    "It is a simple average of absolute percentage errors. The MAPE calculation is as follows:\n",
    "![Mape.svg](attachment:Mape.svg)\n",
    "where $A_t$ is the actual value and $F_t$ is the forecast value. The MAPE is also sometimes reported as a percentage, which is the above equation multiplied by 100. The difference between $A_t$ and $F_t$ is divided by the actual value At again. <br>\n",
    "It **cannot be used if there are zero values** (which sometimes happens for example in demand data) because there would be a division by zero.<br>\n",
    "For forecasts which are too low the percentage error cannot exceed 100%, but **for forecasts which are too high there is no upper limit to the percentage error**.<br>\n",
    "\n",
    "The fuction defination of MAPE is Given below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MAPE(y_true, y_pred): \n",
    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "    return (np.mean(np.abs((y_true - y_pred) / y_true)) * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weighted Mean Absolute error\n",
    "The wMAPE is the metric in which the sales are weighted by sales volumne. Weighted Mean Absolute Percentage Error, as the name suggests, is a **measure that gives greater importance to faster selling products**. Thus it overcomes one of the potential drawbacks of MAPE. There is a very simple way to calculate WMAPE. This involves adding together the absolute errors at the detailed level, then calculating the total of the errors as a percentage of total sales.  This method of calculation leads to the additional benefit that it is robust to individual instances when the base is zero, thus overcoming the divide by zero problem that often occurs with MAPE.\n",
    "\n",
    "WMAPE is a highly useful measure and is becoming increasingly popular both in corporate KPIs and for operational use. It is easily calculated and gives a concise forecast accuracy measurement that can be used to summarise performance at any detailed level across any grouping of products and/or time periods. If a measure of accuracy required this is calculated as 100 - WMAPE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, in order to show how to run the different Accuracy metrics, we will be prforming Random Forest Regression on a dataset the download link for which is given below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009<br>\n",
    "We will start by importing the necessary libraries and the required dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Importing the libraries \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn import feature_extraction, linear_model, model_selection, preprocessing\n",
    "from sklearn.metrics import accuracy_score\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('dataset/winequality-red.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our next step is to divide the data into “attributes” and “labels”. X variable contains all the attributes/features and y variable contains labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates','alcohol']]\n",
    "y = data['quality']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we split 80% of the data to the training set while 20% of the data to test set using below code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will run random forest regression on the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=1000, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import RF Regressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# Instantiate model with 1000 decision trees\n",
    "rf = RandomForestRegressor(n_estimators = 1000, random_state = 42)\n",
    "# Train the model on training data\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's do prediction on test data.\n",
    "\n",
    "y_pred = rf.predict(X_test) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _Code_\n",
    "Now we can calculate the accuracy of our model using the different accuracy metrics that we have explained above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_____________________________________\n",
      "Explained variance Score: [0.45337001]\n",
      "_____________________________________\n",
      "Max Error: 2.615\n",
      "_____________________________________\n",
      "Mean Absolute Error: 0.40428437500000003\n",
      "_____________________________________\n",
      "Mean Squared Error: 0.318432278125\n",
      "_____________________________________\n",
      "Root Mean Squared Error: 0.5642980401569724\n",
      "_____________________________________\n",
      "Mean Squared Log Error: 0.008019925336798525\n",
      "_____________________________________\n",
      "Median Absolute Error: 0.3089999999999997\n",
      "_____________________________________\n",
      "Mean Percentage Error: -0.024683098958333333\n",
      "_____________________________________\n",
      "Mean Absolute Percentage Error: 7.658587425595238\n"
     ]
    }
   ],
   "source": [
    "#Error Calculations\n",
    "from sklearn import metrics\n",
    "#Explained Variance Score\n",
    "print(\"_____________________________________\")\n",
    "print('Explained variance Score:', metrics.explained_variance_score(y_test, y_pred, multioutput='raw_values'))\n",
    "explained_variance_score= metrics.explained_variance_score(y_test, y_pred, multioutput='raw_values')\n",
    "#Max Error\n",
    "print(\"_____________________________________\")\n",
    "print('Max Error:', metrics.max_error(y_test, y_pred))\n",
    "max_error=metrics.max_error(y_test, y_pred)\n",
    "#Mean Absolute Error\n",
    "print(\"_____________________________________\")\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "mean_absolute_error=metrics.mean_absolute_error(y_test, y_pred)\n",
    "#Mean Squared Error\n",
    "print(\"_____________________________________\")\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred)) \n",
    "mean_squared_error=metrics.mean_squared_error(y_test, y_pred)\n",
    "#Root Mean Squared Error\n",
    "print(\"_____________________________________\")\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "root_mean_squared_error=np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
    "#Mean Squared Log Error\n",
    "print(\"_____________________________________\")\n",
    "print('Mean Squared Log Error:', metrics.mean_squared_log_error(y_test, y_pred))\n",
    "mean_squared_log_error=metrics.mean_squared_log_error(y_test, y_pred)\n",
    "#Median Absolute Error\n",
    "print(\"_____________________________________\")\n",
    "print('Median Absolute Error:', metrics.median_absolute_error(y_test, y_pred))\n",
    "median_absolute_error=metrics.median_absolute_error(y_test, y_pred)\n",
    "#Median Absolute Error\n",
    "print(\"_____________________________________\")\n",
    "print('Mean Percentage Error:', MPE(y_test, y_pred))\n",
    "mean_percentage_error=MPE(y_test, y_pred)\n",
    "#Median Absolute Error\n",
    "print(\"_____________________________________\")\n",
    "print('Mean Absolute Percentage Error:', MAPE(y_test, y_pred))\n",
    "mean_absolute_percentage_error=MAPE(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dataframe of errors given by all the metrics explained above\n",
    "data={\"Error Metric\":['Explained variance Score', 'Max Error', 'Mean Absolute Error', 'Mean Squared Error', 'Root Mean Squared Error','Mean Squared Log Error','Median Absolute Error','Mean Percentage Error','Mean Absolute Percentage Error'],\n",
    "      \"Score\":[\"%.4f\" % explained_variance_score, \"%.4f\" % max_error, \"%.4f\" % mean_absolute_error, \"%.4f\" % mean_squared_error, \"%.4f\" % root_mean_squared_error, \"%.4f\" % mean_squared_log_error, \"%.4f\" % median_absolute_error, \"%.4f\" % mean_percentage_error, \"%.4f\" % mean_absolute_percentage_error]}\n",
    "error_df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Error Metric</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Explained variance Score</td>\n",
       "      <td>0.4534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Max Error</td>\n",
       "      <td>2.6150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mean Absolute Error</td>\n",
       "      <td>0.4043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mean Squared Error</td>\n",
       "      <td>0.3184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Root Mean Squared Error</td>\n",
       "      <td>0.5643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mean Squared Log Error</td>\n",
       "      <td>0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Median Absolute Error</td>\n",
       "      <td>0.3090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mean Percentage Error</td>\n",
       "      <td>-0.0247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mean Absolute Percentage Error</td>\n",
       "      <td>7.6586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Error Metric    Score\n",
       "0        Explained variance Score   0.4534\n",
       "1                       Max Error   2.6150\n",
       "2             Mean Absolute Error   0.4043\n",
       "3              Mean Squared Error   0.3184\n",
       "4         Root Mean Squared Error   0.5643\n",
       "5          Mean Squared Log Error   0.0080\n",
       "6           Median Absolute Error   0.3090\n",
       "7           Mean Percentage Error  -0.0247\n",
       "8  Mean Absolute Percentage Error   7.6586"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tips for Metric Selection\n",
    "- The **MAE is also the most intuitive of the metrics** one should by just looking at the absolute difference between the data and the model’s predictions<br>\n",
    "\n",
    "\n",
    "- MAE does not indicate underperformance or overperformance of the model (whether or not the model under or overshoots actual data). Each residual contributes proportionally to the total amount of error, meaning that larger errors will contribute linearly to the overall error<br> \n",
    "\n",
    "\n",
    "- A **small MAE** suggests the model is great at prediction, while a **large MAE** suggests that, model may have trouble in certain areas. A MAE of 0 means that your model is a perfect predictor of the outputs (but this will almost never happen)<br><br>\n",
    "\n",
    "\n",
    "- While the MAE is easily interpretable, using the absolute value of the residual often is not as desirable as squaring this difference. Depending on how the model to treat outliers, or extreme values, in the data, one may want to bring more attention to these outliers or downplay them. The issue of outliers can play a major role in which error metric you use<br>\n",
    "\n",
    "\n",
    "- Because of squaring the difference, the **MSE will almost always be bigger than the MAE**. For this reason, we cannot directly compare the MAE to the MSE. **one can only compare model’s error metrics to those of a competing model**<br>\n",
    "\n",
    "- The effect of the square term in the MSE equation is most apparent with the presence of outliers in our data. **While each residual in MAE contributes proportionally to the total error, the error grows quadratically in MSE**<br><br>\n",
    "\n",
    "\n",
    "- RMSE is the square root of the MSE. Because the **MSE is squared, its units do not match that of the original output**. Researchers will often use **RMSE to convert the error metric** back into similar units, making interpretation easier<br>\n",
    "\n",
    "\n",
    "- Taking the square root before they are averaged, **RMSE gives a relatively high weight to large errors**, so RMSE should be useful when large errors are undesirable.<br><br>\n",
    "\n",
    "\n",
    "- Just as MAE is the average magnitude of error produced by your model, the **MAPE is how far the model’s predictions are off** from their corresponding outputs on average. Like MAE, MAPE also has a clear interpretation since percentages are easier for people to conceptualize. **Both MAPE and MAE are robust to the effects of outliers**<br>\n",
    "\n",
    "\n",
    "- Many of MAPE’s weaknesses actually stem from use division operation. MAPE is **undefined for data points where the value is 0**. Similarly, the MAPE can grow unexpectedly large if the actual values are exceptionally small themselves<br>\n",
    "\n",
    "\n",
    "- Finally, the MAPE is **biased towards predictions** that are systematically less than the actual values themselves. That is to say, MAPE will be lower when the prediction is lower than the actual compared to a prediction that is higher by the same amount. \n",
    "\n",
    "The table given below can also be helpful in regard to metric understanding:\n",
    "\n",
    "\n",
    "|Acroynm|Full Name|Residual Operation?|Robust To Outliers|\n",
    "|-------|---------|-------------------|------------------|\n",
    "|MAE|Mean Absolute Error|Absolute Value|Yes|\n",
    "|MSE|Mean Squared Error\tSquare|No|No|\n",
    "|RMSE|Root Mean Squared Error|Square|No|\n",
    "|MAPE|Mean Absolute Percentage Error|Absolute Value|Yes|\n",
    "|MPE|Mean Percentage Error|N/A|Yes|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation\n",
    "Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample.\n",
    "Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**K-Folds Cross Validation**\n",
    "<br>In K-Folds Cross Validation we split our data into k different subsets (or folds). We use k-1 subsets to train our data and leave the last subset (or the last fold) as test data. We then average the model against each of the folds and then finalize our model. After that we test it against the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits = 5, shuffle = True, random_state = 2)\n",
    "for train_index, test_index in kf.split(X):\n",
    "    # Split train-test\n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.39817868338557993\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators = 100, random_state = 42)\n",
    "#Train the model on training data\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred_kfold = rf.predict(X_test)\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred_kfold)) \n",
    "mean_squared_error=metrics.mean_squared_error(y_test, y_pred_kfold)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# StratifiedKFold\n",
    "StratifiedKFold is a variation of KFold. First, StratifiedKFold shuffles your data, after that splits the data into n_splits parts and Done. Now, it will use each part as a test set. Note that it only and always shuffles data one time before splitting.\n",
    "\n",
    "\n",
    "**Difference in KFold and ShuffleSplit output**\n",
    "\n",
    "KFold will divide your data set into prespecified number of folds, and every sample must be in one and only one fold. A fold is a subset of your dataset.\n",
    "\n",
    "ShuffleSplit will randomly sample your entire dataset during each iteration to generate a training set and a test set. The test_size and train_size parameters control how large the test and training test set should be for each iteration. Since you are sampling from the entire dataset during each iteration, values selected during one iteration, could be selected again during another iteration.\n",
    "\n",
    "Summary: ShuffleSplit works iteratively, KFold just divides the dataset into k folds.\n",
    "\n",
    "**Difference when doing validation**\n",
    "\n",
    "In KFold, during each round you will use one fold as the test set and all the remaining folds as your training set. However, in ShuffleSplit, during each round n you should only use the training and test set from iteration n. As your data set grows, cross validation time increases, making shufflesplits a more attractive alternate. If you can train your algorithm, with a certain percentage of your data as opposed to using all k-1 folds, ShuffleSplit is an attractive option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "skf = StratifiedKFold(n_splits=5, random_state=None)\n",
    "# X is the feature set and y is the target\n",
    "for train_index, test_index in skf.split(X,y):\n",
    "    # Split train-test\n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators = 100, random_state = 42)\n",
    "# Train the model on training data\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.4155485893416928\n"
     ]
    }
   ],
   "source": [
    "y_pred_kfold = rf.predict(X_test)\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred_kfold)) \n",
    "mean_squared_error=metrics.mean_squared_error(y_test, y_pred_kfold)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy Metrics for Binary Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The True and Fake news classification dataset will be used to show how model evaluation for Binary Classification Can be done. The dataset can be downloaded from the link below:<br>\n",
    "https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "true = pd.read_csv(r'dataset/True.csv')\n",
    "fake = pd.read_csv(r'dataset/fake.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of dataset (44898, 5)\n"
     ]
    }
   ],
   "source": [
    "fake['target'] = 'fake'\n",
    "true['target'] = 'true'\n",
    "news = pd.concat([fake, true]).reset_index(drop = True)\n",
    "print('The size of dataset',news.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>subject</th>\n",
       "      <th>date</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donald Trump Sends Out Embarrassing New Year’...</td>\n",
       "      <td>Donald Trump just couldn t wish all Americans ...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 31, 2017</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Drunk Bragging Trump Staffer Started Russian ...</td>\n",
       "      <td>House Intelligence Committee Chairman Devin Nu...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 31, 2017</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sheriff David Clarke Becomes An Internet Joke...</td>\n",
       "      <td>On Friday, it was revealed that former Milwauk...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 30, 2017</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Trump Is So Obsessed He Even Has Obama’s Name...</td>\n",
       "      <td>On Christmas day, Donald Trump announced that ...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 29, 2017</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pope Francis Just Called Out Donald Trump Dur...</td>\n",
       "      <td>Pope Francis used his annual Christmas Day mes...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 25, 2017</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0   Donald Trump Sends Out Embarrassing New Year’...   \n",
       "1   Drunk Bragging Trump Staffer Started Russian ...   \n",
       "2   Sheriff David Clarke Becomes An Internet Joke...   \n",
       "3   Trump Is So Obsessed He Even Has Obama’s Name...   \n",
       "4   Pope Francis Just Called Out Donald Trump Dur...   \n",
       "\n",
       "                                                text subject  \\\n",
       "0  Donald Trump just couldn t wish all Americans ...    News   \n",
       "1  House Intelligence Committee Chairman Devin Nu...    News   \n",
       "2  On Friday, it was revealed that former Milwauk...    News   \n",
       "3  On Christmas day, Donald Trump announced that ...    News   \n",
       "4  Pope Francis used his annual Christmas Day mes...    News   \n",
       "\n",
       "                date target  \n",
       "0  December 31, 2017   fake  \n",
       "1  December 31, 2017   fake  \n",
       "2  December 30, 2017   fake  \n",
       "3  December 29, 2017   fake  \n",
       "4  December 25, 2017   fake  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(news['text'], news.target, test_size=0.2, random_state=2020)\n",
    "# y_test_copy =y_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 99.05%\n"
     ]
    }
   ],
   "source": [
    "pipe = Pipeline([('vect', CountVectorizer()),\n",
    "                 ('tfidf', TfidfTransformer()),\n",
    "                 ('model', RandomForestClassifier())])\n",
    "\n",
    "model = pipe.fit(x_train, y_train)\n",
    "prediction = model.predict(x_test)\n",
    "print(\"accuracy: {}%\".format(round(accuracy_score(y_test, prediction)*100,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 99.05%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(\"accuracy: {}%\".format(round(accuracy_score(y_test, prediction)*100,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen’s kappa \n",
    "\n",
    "It is a statistic that **measures inter-annotator agreement**.\n",
    "\n",
    "This function computes Cohen’s kappa, a **score that expresses the level of agreement between two annotators** on a classification problem. It is defined as:<<br>\n",
    "$[\\kappa = (p_o - p_e) / (1 - p_e)]$<br>\n",
    "where  is the empirical probability of agreement on the label assigned to any sample (the observed agreement ratio), and  is the expected agreement when both annotators assign labels randomly.  is estimated using a per-annotator empirical prior over the class labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9810118237654452"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cohen_kappa_score(y_test, prediction)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hamming Loss\n",
    "The hamming_loss computes the average Hamming loss or **Hamming distance between two sets of samples**.\n",
    "\n",
    "If $hat{y}_j$ is the predicted value for the $j$-th label of a given sample, $y_j$ is the corresponding true value, and $n_text{labels}$ is the number of classes or labels, then the Hamming loss $L_{Hamming}$ between two samples is defined as:<br>\n",
    "$L_{Hamming}(y, \\hat{y}) = \\frac{1}{n_\\text{labels}} \\sum_{j=0}^{n_\\text{labels} - 1} 1(\\hat{y}_j \\not= y_j)$<br>\n",
    "where $1(x)$ is the indicator function.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009465478841870824"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import hamming_loss\n",
    "hamming_loss(y_test, prediction)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "A confusion matrix is a summary of prediction results on a classification problem. The number of correct and incorrect predictions are summarized with count values and broken down by each class. This is the key to the confusion matrix. The confusion matrix shows the ways in which your classification model is confused when it makes predictions. It gives us insight not only into the errors being made by a classifier but more importantly the types of errors that are being made."
   ]
  },
  {
   "attachments": {
    "confusion%20matrix.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![confusion%20matrix.PNG](attachment:confusion%20matrix.PNG)\n",
    "\n",
    "Here,\n",
    "\n",
    "<br>Class 1 : Positive\n",
    "<br>Class 2 : Negative\n",
    "\n",
    "**Definition of the Terms:**\n",
    "\n",
    "<br>Positive (P) : Observation is positive (for example: is an apple).\n",
    "<br>Negative (N) : Observation is not positive (for example: is not an apple).\n",
    "<br>True Positive (TP) : Observation is positive, and is predicted to be positive.\n",
    "<br>False Negative (FN) : Observation is positive, but is predicted negative.\n",
    "<br>True Negative (TN) : Observation is negative, and is predicted to be negative.\n",
    "<br>False Positive (FP) : Observation is negative, but is predicted positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Confusion Matix')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using plot_confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "table2=confusion_matrix(y_test, prediction)\n",
    "#using seaborn\n",
    "# fake = 0, true = 1\n",
    "import seaborn as sns\n",
    "ax = sns.heatmap(table2,annot=True,cmap='Blues', fmt='g')\n",
    "ax.set(xlabel=\"Predticted label\", ylabel = \"True label\")\n",
    "ax.set_title('Confusion Matix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Precision-Recall (PR) Curve\n",
    "A PR curve is simply a graph with **Precision values on the y-axis and Recall values on the x-axis**. In other words, the PR curve contains TP/(TP+FN) on the y-axis and TP/(TP+FP) on the x-axis.\n",
    "\n",
    "1. It is important to note that Precision is also called the Positive Predictive Value (PPV).\n",
    "2. Recall is also called Sensitivity, Hit Rate or True Positive Rate (TPR)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2-class Precision-Recall curve')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_precision_recall_curve\n",
    "disp = plot_precision_recall_curve(model, x_test, y_test)\n",
    "disp.ax_.set_title('2-class Precision-Recall curve')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC (Receiver Operating Characteristics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROC Curves are used to see how well your classifier can separate positive and negative examples and to identify the best threshold for separating them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In ROC curves, the true positive rate (TPR, y-axis) is plotted against the false positive rate (FPR, x-axis). These quantities are defined as follows:<br>\n",
    "**TPR(True Positve Rate) = TP/(TP+FN)<br>\n",
    "FPR(False Positve Rate) = FP/(TN+FP)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre = pd.Series(prediction)\n",
    "y_test.replace(to_replace = 'true',value=1,inplace = True)\n",
    "y_test.replace(to_replace = 'fake',value= 0,inplace = True)\n",
    "pre.replace(to_replace = 'fake',value= 0,inplace = True,)\n",
    "pre.replace(to_replace = 'true',value= 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9905486227211209\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, prediction)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "print(roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.title('ROC Curve')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AUC (Area Under the Curve)\n",
    "The model performance is determined by looking at the area under the ROC curve (or AUC). **An excellent model has AUC near to the 1.0**, which means it has a good measure of separability. For your model, the AUC is the combined are of the blue, green and purple rectangles,\n",
    "<br>so the AUC = 0.4 x 0.6 + 0.2 x 0.8 + 0.4 x 1.0 = 0.80."
   ]
  },
  {
   "attachments": {
    "AUC.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![AUC.PNG](attachment:AUC.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9905486227211209\n"
     ]
    }
   ],
   "source": [
    "# AUC explaned in above illustration \n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "print(roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy Metrics for Multi-Class Classification\n",
    "In multiclass and multilabel classification task, the notions of precision, recall, and F-measures can be applied to each label independently. There are a few ways to combine results across labels, specified by the average argument to the average_precision_score (multilabel only), f1_score, fbeta_score, precision_recall_fscore_support, precision_score and recall_score functions, as described above. <br><br>\n",
    "Note that if all labels are included, “micro”-averaging in a multiclass setting will produce precision, recall and $F$ that are all identical to accuracy. Also note that “weighted” averaging may produce an F-score that is not between precision and recall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuarcy Score\n",
    "The accuracy_score function **computes the accuracy, either the fraction (default) or the count (normalize=False) of correct predictions**.\n",
    "\n",
    "**In multilabel classification, the function returns the subset accuracy**. If the entire set of predicted labels for a sample strictly match with the true set of labels, then the subset accuracy is 1.0; otherwise it is 0.0.\n",
    "\n",
    "If $\\hat{y}_i$ is the predicted value of the $i$-th sample and $y_i$ is the corresponding true value, then the fraction of correct predictions over $n_\\text{samples}$ is defined as:<br>\n",
    "$\\texttt{accuracy}(y, \\hat{y}) = \\frac{1}{n_\\text{samples}} \\sum_{i=0}^{n_\\text{samples}-1} 1(\\hat{y}_i = y_i)$<br>\n",
    "where $1(x)$ is the indicator function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision, recall and F-measures\n",
    "The precision is the ratio **tp / (tp + fp)** where tp is the number of true positives and fp the number of false positives. The precision is intuitively the **ability of the classifier not to label as positive a sample that is negative**.<br><br>\n",
    "The best value is 1 and the worst value is 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **recall** is the ratio **tp / (tp + fn)** where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the **ability of the classifier to find all the positive samples**.\n",
    "\n",
    "The best value is 1 and the worst value is 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **F-measure** $(F_\\beta$ and $F_1$ measures) can be interpreted as a weighted harmonic mean of the precision and recall. A $F_\\beta$ measure reaches its best value at 1 and its worst score at 0. With $\\beta = 1$, $F_\\beta$ and $F_1$ are equivalent, and the recall and the precision are equally important.\n",
    "\n",
    "The **average_precision_score** function computes the average precision (AP) from prediction scores. The value is between 0 and 1 and higher is better. AP is defined as: <br>\n",
    "$\\text{AP} = \\sum_n (R_n - R_{n-1}) P_n$<br>\n",
    "where $P_n$ and $R_n$ are the precision and recall at the nth threshold. With random predictions, the AP is the fraction of positive samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To show how these metrics can evaluate our model, we first import the dataset for multiclass classification. We will be using the fruit dataset for classification of fruits on the basis of the features such as mass, height, etc. The dataframe can be downloaded from the following link:<br> https://github.com/susanli2016/Machine-Learning-with-Python/blob/master/fruit_data_with_colors.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fruit_label fruit_name fruit_subtype  mass  width  height  color_score\n",
       "0            1      apple  granny_smith   192    8.4     7.3         0.55\n",
       "1            1      apple  granny_smith   180    8.0     6.8         0.59\n",
       "2            1      apple  granny_smith   176    7.4     7.2         0.60\n",
       "3            2   mandarin      mandarin    86    6.2     4.7         0.80\n",
       "4            2   mandarin      mandarin    84    6.0     4.6         0.79"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits = pd.read_table(r'dataset/Fruit.txt')\n",
    "fruits.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 59 pieces of fruits, 7 features in the dataset and 4 types of fruits in the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15,)\n",
      "(15, 4)\n"
     ]
    }
   ],
   "source": [
    "#Specify Targets and features\n",
    "feature_names = ['mass', 'width', 'height', 'color_score']\n",
    "X = fruits[feature_names]\n",
    "y = fruits['fruit_label']\n",
    "\n",
    "#split data into test and train\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "#Scaling Our data\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "print(y_test.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import RF Regressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "#fit the model\n",
    "model = rf.fit(X_train, y_train)\n",
    "\n",
    "#Make predictions\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The methods that we have explained above have been demonstrated in the block below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 86.67%\n",
      "Precision Score:  0.8666666666666666\n",
      "Recall_score:  0.8666666666666667\n",
      "f1_score:  0.8675132275132273\n",
      "fbeta_score:  0.8712797619047619\n",
      "precision_recall_fscore_support:  (array([0.8       , 1.        , 1.        , 0.66666667]), array([1.  , 1.  , 0.75, 1.  ]), array([0.83333333, 1.        , 0.9375    , 0.71428571]), array([4, 1, 8, 2], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"accuracy: {}%\".format(round(accuracy_score(y_test, y_pred)*100,2)))\n",
    "print(\"Precision Score: \", metrics.precision_score(y_test, y_pred, average='macro'))\n",
    "print(\"Recall_score: \", metrics.recall_score(y_test, y_pred, average='micro'))\n",
    "print(\"f1_score: \", metrics.f1_score(y_test, y_pred, average='weighted'))\n",
    "print(\"fbeta_score: \", metrics.fbeta_score(y_test, y_pred, average='macro', beta=0.5))\n",
    "print(\"precision_recall_fscore_support: \", metrics.precision_recall_fscore_support(y_test, y_pred, beta=0.5, average=None))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix for MultiClass\n",
    "The multilabel_confusion_matrix function **computes class-wise (default) or sample-wise (samplewise=True) multilabel confusion matrix to evaluate the accuracy of a classification**. multilabel_confusion_matrix also treats multiclass data as if it were multilabel, as this is a transformation commonly applied to evaluate multiclass problems with binary classification metrics (such as precision, recall, etc.).\n",
    "\n",
    "When calculating class-wise multilabel confusion matrix $C$, the count of true negatives for class $i$ is $C_{i,0,0}$, false negatives is $C_{i,1,0}$, true positives is $C_{i,1,1}$ and false positives is $C_{i,0,1}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[4 0 0 0]\n",
      " [0 1 0 0]\n",
      " [1 0 6 1]\n",
      " [0 0 0 2]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix\n",
    "multilabel_confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "titles_options = [(\"Confusion matrix, without normalization\", None)\n",
    "                 ]\n",
    "for title, normalize in titles_options:\n",
    "    disp = plot_confusion_matrix(model, X_test, y_test,\n",
    "                                 cmap=plt.cm.GnBu,\n",
    "                                 normalize=normalize)\n",
    "    disp.ax_.set_title(title)\n",
    "\n",
    "    print(title)\n",
    "    print(disp.confusion_matrix)\n",
    "    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC and AUC\n",
    "An example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality.\n",
    "\n",
    "**ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis**. This means that the top left corner of the plot is the “ideal” point - a false positive rate of zero, and a true positive rate of one. This is not very realistic, but it does mean that a larger area under the curve (AUC) is usually better.\n",
    "\n",
    "The “steepness” of ROC curves is also important, since it is ideal to maximize the true positive rate while minimizing the false positive rate.\n",
    "\n",
    "**ROC curves are typically used in binary classification** to study the output of a classifier. In order to extend ROC curve and ROC area to multi-label classification, **it is necessary to binarize the output**. **One ROC curve can be drawn per label**, but one can also draw a ROC curve by considering each element of the label indicator matrix as a binary prediction (micro-averaging).\n",
    "\n",
    "**Another evaluation measure for multi-label classification is macro-averaging, which gives equal weight to the classification of each label**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# print(__doc__)\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from scipy import interp\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Import some data just check which format is in the file before executing this code\n",
    "fruits = pd.read_table(r'dataset/Fruit.txt')\n",
    "feature_names = ['mass', 'width', 'height', 'color_score']\n",
    "X = fruits[feature_names]\n",
    "y = fruits['fruit_label']\n",
    "# Binarize the output\n",
    "Y_1 = label_binarize(y, classes=[1, 2, 3, 4])\n",
    "n_classes = Y_1.shape[1]\n",
    "\n",
    "# Add noisy features to make the problem harder\n",
    "# random_state = np.random.RandomState(0)\n",
    "# n_samples, n_features = X.shape\n",
    "# X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]\n",
    "\n",
    "# shuffle and split training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y_1, test_size=.5,\n",
    "                                                    random_state=0)\n",
    "\n",
    "# Learn to predict each class against the other\n",
    "classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,\n",
    "                                 random_state= 0))\n",
    "y_score = classifier.fit(X_train, y_train).decision_function(X_test)\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_test.ravel(), y_score.ravel())\n",
    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot of a ROC curve for a specific class has been shown below\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "lw = 2\n",
    "plt.plot(fpr[2], tpr[2], color='darkorange',\n",
    "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2])\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.text(0.41,0.8,'More accurate area',fontsize = 12)\n",
    "plt.text(0.63,0.4,'Less accurate area',fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot ROC curves for all the classes ie a multilabel problem is shown below. It Computes macro-average ROC curve and ROC area. The Defference in Macro-average and micro-average is explained after the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits.fruit_label.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First aggregate all false positive rates\n",
    "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
    "\n",
    "# Then interpolate all ROC curves at this points\n",
    "mean_tpr = np.zeros_like(all_fpr)\n",
    "for i in range(n_classes):\n",
    "    mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
    "\n",
    "# Finally average it and compute AUC\n",
    "mean_tpr /= n_classes\n",
    "\n",
    "fpr[\"macro\"] = all_fpr\n",
    "tpr[\"macro\"] = mean_tpr\n",
    "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
    "\n",
    "# Plot all ROC curves\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
    "         label='micro-average ROC curve (area = {0:0.2f})'\n",
    "               ''.format(roc_auc[\"micro\"]),\n",
    "         color='black', linestyle=':', linewidth=4)\n",
    "\n",
    "plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
    "         label='macro-average ROC curve (area = {0:0.2f})'\n",
    "               ''.format(roc_auc[\"macro\"]),\n",
    "         color='grey', linestyle=':', linewidth=4)\n",
    "\n",
    "colors = cycle(['lightskyblue', 'darkorange', 'gold', 'yellowgreen'])\n",
    "for i, color in zip(range(n_classes), colors):\n",
    "    plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n",
    "             label='ROC curve of class {0} (area = {1:0.2f})'\n",
    "             ''.format(i, roc_auc[i]))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Some extension of Receiver operating characteristic to multi-class')\n",
    "plt.legend(loc=\"best\")\n",
    "plt.text(0.4,0.6,'More accurate area',fontsize = 12, rotation=45)\n",
    "plt.text(0.63,0.4,'Less accurate area',fontsize = 12, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Area under ROC for the multiclass problem**\n",
    "\n",
    "The func:`sklearn.metrics.roc_auc_score` function can be used for\n",
    "multi-class classification. The multi-class One-vs-One scheme compares every\n",
    "unique pairwise combination of classes. In this section, we calculate the AUC\n",
    "using the OvR and OvO schemes. We report a macro average, and a\n",
    "prevalence-weighted average.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One-vs-One ROC AUC scores:\n",
      "0.858890 (macro),\n",
      "0.797464 (weighted by prevalence)\n",
      "One-vs-Rest ROC AUC scores:\n",
      "0.858890 (macro),\n",
      "0.797464 (weighted by prevalence)\n"
     ]
    }
   ],
   "source": [
    "y_prob = classifier.predict_proba(X_test)\n",
    "\n",
    "macro_roc_auc_ovo = roc_auc_score(y_test, y_prob, multi_class=\"ovo\",\n",
    "                                  average=\"macro\")\n",
    "weighted_roc_auc_ovo = roc_auc_score(y_test, y_prob, multi_class=\"ovo\",\n",
    "                                     average=\"weighted\")\n",
    "macro_roc_auc_ovr = roc_auc_score(y_test, y_prob, multi_class=\"ovr\",\n",
    "                                  average=\"macro\")\n",
    "weighted_roc_auc_ovr = roc_auc_score(y_test, y_prob, multi_class=\"ovr\",\n",
    "                                     average=\"weighted\")\n",
    "print(\"One-vs-One ROC AUC scores:\\n{:.6f} (macro),\\n{:.6f} \"\n",
    "      \"(weighted by prevalence)\"\n",
    "      .format(macro_roc_auc_ovo, weighted_roc_auc_ovo))\n",
    "print(\"One-vs-Rest ROC AUC scores:\\n{:.6f} (macro),\\n{:.6f} \"\n",
    "      \"(weighted by prevalence)\"\n",
    "      .format(macro_roc_auc_ovr, weighted_roc_auc_ovr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision Recall Curve for Multiclass\n",
    "The precision recall curve with the fruit dataset is shown below<br>\n",
    "Precision-recall curves are typically used in binary classification to study the output of a classifier. In order to extend the precision-recall curve and average precision to multi-class or multi-label classification, it is necessary to binarize the output. One curve can be drawn per label, but one can also draw a precision-recall curve by considering each element of the label indicator matrix as a binary prediction (micro-averaging).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import warnings\n",
    "\n",
    "# Import some data \n",
    "fruits = pd.read_table(r'dataset/Fruit.txt')\n",
    "feature_names = ['mass', 'width', 'height', 'color_score']\n",
    "X = fruits[feature_names]\n",
    "y = fruits['fruit_label']\n",
    "random_state = np.random.RandomState(0)\n",
    "\n",
    "\n",
    "\n",
    "# Limit to the two first classes, and split into training and test\n",
    "# shuffle and split training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,\n",
    "                                                    random_state=0)\n",
    "# Use label_binarize to be multi-label like settings\n",
    "Y = label_binarize(y, classes=[0, 1, 2])\n",
    "n_classes = Y.shape[1]\n",
    "\n",
    "# Split into training and test\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5,\n",
    "                                                    random_state=random_state)\n",
    "\n",
    "# We use OneVsRestClassifier for multi-label prediction\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "# Run classifier\n",
    "classifier = OneVsRestClassifier(svm.LinearSVC(random_state=random_state))\n",
    "classifier.fit(X_train, Y_train)\n",
    "y_score = classifier.decision_function(X_test)\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average precision score in multi-label settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision score, micro-averaged over all classes: 0.34\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "# For each class\n",
    "precision = dict()\n",
    "recall = dict()\n",
    "average_precision = dict()\n",
    "for i in range(n_classes):\n",
    "    precision[i], recall[i], _ = precision_recall_curve(Y_test[:, i],\n",
    "                                                        y_score[:, i])\n",
    "    average_precision[i] = average_precision_score(Y_test[:, i], y_score[:, i])\n",
    "\n",
    "# A \"micro-average\": quantifying score on all classes jointly\n",
    "precision[\"micro\"], recall[\"micro\"], _ = precision_recall_curve(Y_test.ravel(),\n",
    "    y_score.ravel())\n",
    "average_precision[\"micro\"] = average_precision_score(Y_test, y_score,\n",
    "                                                     average=\"micro\")\n",
    "print('Average precision score, micro-averaged over all classes: {0:0.2f}'\n",
    "      .format(average_precision[\"micro\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the micro-averaged Precision-Recall curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average precision score, micro-averaged over all classes: AP=0.34')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.step(recall['micro'], precision['micro'], where='post')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title(\n",
    "    'Average precision score, micro-averaged over all classes: AP={0:0.2f}'\n",
    "    .format(average_precision[\"micro\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot Precision-Recall curve for each class and iso-f1 curves\n",
    ".............................................................\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "# setup plot details\n",
    "colors = cycle(['navy', 'turquoise', 'darkorange', 'cornflowerblue', 'teal'])\n",
    "\n",
    "plt.figure(figsize=(7, 8))\n",
    "f_scores = np.linspace(0.2, 0.8, num=4)\n",
    "lines = []\n",
    "labels = []\n",
    "for f_score in f_scores:\n",
    "    x = np.linspace(0.01, 1)\n",
    "    y = f_score * x / (2 * x - f_score)\n",
    "    l, = plt.plot(x[y >= 0], y[y >= 0], color='gray', alpha=0.2)\n",
    "    plt.annotate('f1={0:0.1f}'.format(f_score), xy=(0.9, y[45] + 0.02))\n",
    "\n",
    "lines.append(l)\n",
    "labels.append('iso-f1 curves')\n",
    "l, = plt.plot(recall[\"micro\"], precision[\"micro\"], color='gold', lw=2)\n",
    "lines.append(l)\n",
    "labels.append('micro-average Precision-recall (area = {0:0.2f})'\n",
    "              ''.format(average_precision[\"micro\"]))\n",
    "\n",
    "for i, color in zip(range(n_classes), colors):\n",
    "    l, = plt.plot(recall[i], precision[i], color=color, lw=2)\n",
    "    lines.append(l)\n",
    "    labels.append('Precision-recall for class {0} (area = {1:0.2f})'\n",
    "                  ''.format(i, average_precision[i]))\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.subplots_adjust(bottom=0.25)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Extension of Precision-Recall curve to multi-class')\n",
    "plt.legend(lines, labels, loc=(0, -.38), prop=dict(size=14))\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification Report\n",
    "The classification_report function **builds a text report showing the main classification metrics**. Here is a small example with custom target_names and inferred labels<br>\n",
    "Below there is an example of classification report to give the evaluation of a logistic regression classification model on the fruit dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "# Import some data \n",
    "fruits = pd.read_table(r'dataset/Fruit.txt')\n",
    "feature_names = ['mass', 'width', 'height', 'color_score']\n",
    "X = fruits[feature_names]\n",
    "y = fruits['fruit_label']\n",
    "#fir Logistic Regression\n",
    "LogReg = LogisticRegression()\n",
    "model = LogReg.fit(X_train, y_train)\n",
    "prediction = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.37      1.00      0.54         7\n",
      "           2       1.00      1.00      1.00         3\n",
      "           3       1.00      0.14      0.25        14\n",
      "           4       1.00      1.00      1.00         6\n",
      "\n",
      "    accuracy                           0.60        30\n",
      "   macro avg       0.84      0.79      0.70        30\n",
      "weighted avg       0.85      0.60      0.54        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#Model-Evaluation\" role=\"tab\" aria-controls=\"messages\">Go to top<span class=\"badge badge-primary badge-pill\"></span></a>\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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