I got the core implementation down pretty fast, fit just stores the data, predict loops over training points and computes L2 distance.
Start by clarifying the problem scope (e.g., Euclidean distance, binary vs. multiclass) and then implement a simple 1-NN classifier with fit storing training data and predict computing distances to all points. After coding, discuss tie-breaking strategies, time complexity, and how to extend to K-NN by tracking the k smallest distances and using majority vote.
Pro tip: Mention that for large datasets, using a KD-tree or Ball tree can reduce prediction time from O(n) to O(log n) on average, but note that this is an optimization beyond the basic implementation.
Ask about distance metric (default Euclidean), data types (numeric features), and whether the implementation should be efficient or just correct. Confirm that fit stores training data and predict returns labels.
For fit, simply store X_train and y_train. For predict, for each test point compute distances to all training points, find the index of the minimum distance, and return the corresponding label.
Discuss strategies: choose the label of the first occurrence, random choice, or use a secondary criterion like smallest sum of distances. Mention that ties are rare with continuous data but possible with discrete features.
Fit is O(1) time and O(n*d) space. Predict is O(n*d) per query (or O(n*d) for batch). Compare to K-NN which adds O(k) for maintaining top-k, but same asymptotic complexity.
Modify predict to find the k nearest neighbors (e.g., using a max-heap of size k), then take majority vote (or distance-weighted vote). Discuss handling ties in voting and choice of k.
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