How KNeighborsClassifier works

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 Good question 👍 Let’s break it down.

How KNeighborsClassifier works

Where’s the bottleneck?

  • Training? ❌ Cheap (just memorizing data).

  • Choosing best k? ❌ That’s a hyperparameter tuning task, not the core bottleneck.

  • Calculating class probabilities? ❌ Very light compared to distance computations.

  • Storing and searching through all training samples at prediction time? ✅ YES.

That’s the heavy part: computing distances from a test point to all training samples (especially in high dimensions / large datasets).

✅ Correct Answer

Storing and searching through all training samples at prediction time.

⚡ Pro Tip: To speed up KNN, people often use KD-Trees, Ball Trees, or Approximate Nearest Neighbors (ANN) instead of brute-force search.

Would you like me to show a quick time complexity comparison of KNN training vs prediction?

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