K-Nearest Neighbors Visualization

Step 1: Proximity Calculation - For a new data point, calculate its distance to all training points using the chosen distance metric (Euclidean, Manhattan, etc.).

Step 2: Neighbor Selection - Select the K training points closest to the new data point as its "neighbors".

Step 3: Voting - The new point is classified based on the majority class among its K neighbors. This can be uniform (each neighbor has equal vote) or weighted by distance (closer neighbors have more influence).

Key Parameters:

• K value: The number of neighbors to consider. Larger values smooth decision boundaries and reduce noise sensitivity, but may miss local patterns.
• Distance metric: How similarity between points is measured. Common options include Euclidean (straight-line), Manhattan (city block), and Chebyshev (maximum coordinate difference).
• Weighting: Whether all neighbors have equal influence (uniform) or closer neighbors matter more (distance-weighted).

KNN is a "lazy learning" algorithm - it doesn't build a model during training, but stores all training examples and classifies new points at prediction time.