K-Nearest Neighbors (KNN) is a simple machine learning algorithm for classification and regression tasks. It falls under the category of instance-based learning or lazy learning algorithms. KNN is simple and can be effective in scenarios where data is well-clustered or there are clear decision boundaries. However, more advanced algorithms like support vector machines, decision trees, and neural networks often outperform KNN on complex tasks.
Steps…
Data Preparation: Use dataset with labeled examples. Each example consists of a set of features (attributes) and a corresponding class label (for classification) or a target value (for regression).
Choose K: Specify the number of neighbors 'k' to consider when making predictions. This can be done through experimentation or by using techniques like cross-validation to find the optimal value of 'k'.
Distance Metric: A distance metric, such as Euclidean distance, is used to measure the similarity or dissimilarity between data points in the feature space.
Predict Classification: For a classificiation task, KNN inputs the query point, and identifies the 'k' nearest neighbors. It counts the occurrences of each class among these neighbors, the outputs the class label with the highest count.
Predict Regression: For a regression task, KNN calculates the average or weighted average of the target values of the 'k' nearest neighbors, and outputs this value.