k-Nearest Neighbors

35 min
Block 9 — Nonparametric instance-based models
Objective
understand k-nearest neighbors as pure instance-based learning; master distance metrics and when each is appropriate; measure the decisive role of feature scaling; recognize the bias-variance tradeoff controlled by k; understand the curse of dimensionality and its practical remedy; account for computational costs at training and inference; recognize when kNN is the correct model and when it is a symptom of poor feature engineering.
Estimated duration
75 minutes
Prerequisites
chapters 006 (features and target), 007 (labels and classes), 008 (algorithm vs model), 023 (feature scaling), 026 (train-validation-test split), 029 (fit-predict-predict_proba)
Associated quizzes
040.1-quiz-fit-and-lazy-learning.md to 040.6-quiz-when-to-use-knn.md

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This lesson is part of the “Classification Algorithms” module

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