K-Nearest Neighbors
K‑Nearest Neighbors (KNN) is a simple, intuitive algorithm: classify a new point based on the majority class of its k nearest neighbors in the training data.
KNN is a lazy learner – it stores all training data and makes predictions on the fly.
How It Works
1. Choose k (number of neighbors).
2. Calculate distance (usually Euclidean) from new point to all training points.
3. Find k closest points.
4. Assign the majority class among them.
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)Choosing k
- Small k: sensitive to noise, may overfit.
- Large k: smoother decision boundary, may underfit.
- Typical practice: try odd numbers (3,5,7) and pick best via cross‑validation.
Important: Scale Features!
KNN uses distance, so features with large scales dominate. Always apply standardization or normalization before using KNN.
Pros and Cons
✅ Simple, no training phase, works for multi‑class. ❌ Slow for large datasets, requires scaled data, memory intensive.
Two Minute Drill
- KNN classifies by majority vote of k nearest neighbors.
- Distance metric matters (Euclidean default).
- Scale features before using KNN.
- Choose k by cross‑validation.
Need more clarification?
Drop us an email at career@quipoinfotech.com