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add colab links in tutorials REWEIW:NEW
ref:83cf456c69b1848bd51f79a58ed717018ceff68e
1 parent 3a545cb commit 2c16945

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‎apply_model/fast_light_gbm_applier.ipynb

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"source": [
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"# Save LightGBM model in CatBoost format to use fast CatBoost appliers\n",
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"\n",
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/apply_model/fast_light_gbm_applier.ipynb)\n",
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"\n",
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"To save LightGBM in CatBoost format you need to convert LightGBM model to ONNX, and then to convert the model from ONNX to CatBoost.\n",
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"+ Save LightGBM model in the ONNX format\n",
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"+ Load the ONNX model into CatBoost using the load_model() method\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "mypy37",
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"display_name": "Python 3",
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"name": "python3"
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"language_info": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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"version": "3.7.4"
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"nbformat": 4,

‎classification/classification_tutorial.ipynb

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"cell_type": "markdown",
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/classification/classification_tutorial.ipynb)\n",
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"\n",
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"In this tutorial we will use dataset Amazon Employee Access Challenge from [Kaggle](https://www.kaggle.com) competition for our experiments. Data can be downloaded [here](https://www.kaggle.com/c/amazon-employee-access-challenge/data)."
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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"version": "3.7.4"
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‎classification/classification_with_parameter_tuning_tutorial.ipynb

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"source": [
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"# Simple classification example with missing feature handling and parameter tuning\n",
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"\n",
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/classification/classification_with_parameter_tuning_tutorial.ipynb)\n",
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"\n",
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"This tutorial will show you how to use CatBoost to train binary classifier for data with missing feature and how to do hyper-parameter tuning using Hyperopt framework.\n",
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"\n",
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"Gaps in data may be a challenge to handle correctly, especially when they appear in categorical features, this tutorial will also give some advices how to handle them during model application."
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"display_name": "Python 3",
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"language": "python",
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"file_extension": ".py",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.12"
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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"nbformat": 4,

‎competition_examples/mlbootcamp_v_tutorial.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/competition_examples/mlbootcamp_v_tutorial.ipynb)\n",
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"\n",
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"This code shows 7th place solution for competition of forecasting cardiovascular diseases (http://mlbootcamp.ru/round/12/sandbox/)"
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},
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],
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"metadata": {
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.15"
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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‎competition_examples/quora_w2v.ipynb

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"cell_type": "markdown",
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"source": [
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"## Example of using CatBoost on text data with word2vec embedding."
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"## Example of using CatBoost on text data with word2vec embedding.\n",
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"\n",
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/competition_examples/quora_w2v.ipynb)"
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"file_extension": ".py",
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"nbconvert_exporter": "python",
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"version": "2.7.15"
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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"nbformat": 4,

‎cross_validation/cv_tutorial.ipynb

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"# Cross Validation tutorial"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/cross_validation/cv_tutorial.ipynb)"
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"name": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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‎custom_loss/custom_loss_and_metric_tutorial.ipynb

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"# $$User\\ Defined\\ Metrics\\ Tutorial$$"
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/custom_loss/custom_loss_and_metric_tutorial.ipynb)"
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‎events/2019_10_30_odsc_west.ipynb

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"cell_type": "markdown",
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/events/2019_10_30_odsc_west.ipynb)\n",
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"\n",
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"In this tutorial we will use dataset Amazon Employee Access Challenge from [Kaggle](https://www.kaggle.com) competition for our experiments. Data can be downloaded [here](https://www.kaggle.com/c/amazon-employee-access-challenge/data)."
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‎events/2019_11_07_intel_hands_on.ipynb

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"cell_type": "markdown",
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/events/2019_11_07_intel_hands_on.ipynb)\n",
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"\n",
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"In this tutorial we will use dataset Amazon Employee Access Challenge from [Kaggle](https://www.kaggle.com) competition for our experiments. Data can be downloaded [here](https://www.kaggle.com/c/amazon-employee-access-challenge/data)."
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‎events/datastart_moscow_apr_27_2019.ipynb

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"cell_type": "markdown",
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/events/datastart_moscow_apr_27_2019.ipynb)\n",
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"\n",
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"In this tutorial we will use dataset Amazon Employee Access Challenge from [Kaggle](https://www.kaggle.com) competition for our experiments. Data can be downloaded [here](https://www.kaggle.com/c/amazon-employee-access-challenge/data)."
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‎events/intel_hands_on_moscow_oct_11_2019.ipynb

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"cell_type": "markdown",
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/events/intel_hands_on_moscow_oct_11_2019.ipynb)\n",
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"\n",
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"In this tutorial we will use dataset Amazon Employee Access Challenge from [Kaggle](https://www.kaggle.com) competition for our experiments. Data can be downloaded [here](https://www.kaggle.com/c/amazon-employee-access-challenge/data)."
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‎events/pydata_la_oct_21_2018.ipynb

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"cell_type": "markdown",
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/events/pydata_la_oct_21_2018.ipynb)\n",
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"In this tutorial we will use dataset Amazon Employee Access Challenge from [Kaggle](https://www.kaggle.com) competition for our experiments. Data can be downloaded [here](https://www.kaggle.com/c/amazon-employee-access-challenge/data)."
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‎events/pydata_moscow_oct_13_2018.ipynb

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"cell_type": "markdown",
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/events/pydata_moscow_oct_13_2018.ipynb)\n",
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"In this tutorial we will use dataset Amazon Employee Access Challenge from [Kaggle](https://www.kaggle.com) competition for our experiments. Data can be downloaded [here](https://www.kaggle.com/c/amazon-employee-access-challenge/data)."
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‎events/pydata_nyc_oct_19_2018.ipynb

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"cell_type": "markdown",
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/events/pydata_nyc_oct_19_2018.ipynb)\n",
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‎feature_selection/eval_tutorial.ipynb

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"# CatBoost feature selection tutorial"
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catboost/tutorials/blob/master/feature_selection/eval_tutorial.ipynb)"
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