Machine Learning – Unit II: Nearest Neighbor Based ModelsFree

Complete Machine Learning Unit II notes covering Nearest Neighbor-Based Models and proximity measures. Topics include Euclidean, Manhattan and Minkowski distance, non-metric similarity functions, Cosine Similarity, Jaccard Similarity, Hamming Similarity, proximity between binary patterns, distance-based classification, K-Nearest Neighbor (KNN), r-Nearest Neighbors, KNN Regression, confusion matrix, classifier performance metrics, Type 1 and Type 2 errors, and regression performance metrics such as MAE, MSE, RMSE and R².

The notes also include worked examples and diagrams explaining KNN classification, radius-based neighbors, distance calculations, and KNN regression.