61. K-Nearest Neighbors: Judge by Your Company

Short summary

K-Nearest Neighbors classifies new points by storing the entire training set, finding K nearest examples, and taking a majority vote on their class—no model training required. The post explains distance metrics, how K affects bias-variance tradeoff, and why feature scaling is critical. Includes practical Python code examples with cross-validation and real datasets.

• •KNN requires no training phase—it stores data and votes neighbors at inference time
• •K parameter controls bias-variance: small K overfits (high variance), large K underfits (high bias)
• •Feature scaling is critical; unscaled large-range features dominate distance calculations

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