K-Nearest Neighbors Cheat Sheet
A cheat sheet for K-Nearest Neighbors covering classification and regression in scikit-learn, distance metrics, choosing k, and scalability considerations.
1 PageBeginnerMar 18, 2026
Classifier with scikit-learn
Fit a distance-weighted KNN classifier.
python
from sklearn.neighbors import KNeighborsClassifierfrom sklearn.preprocessing import StandardScalerfrom sklearn.pipeline import make_pipelineknn = make_pipeline( StandardScaler(), KNeighborsClassifier(n_neighbors=5, weights='distance', metric='minkowski', p=2))knn.fit(X_train, y_train)print('Accuracy:', knn.score(X_test, y_test))
Choosing k
Use cross-validation to select the best neighbor count.
python
from sklearn.model_selection import cross_val_scoreimport numpy as npscores = []for k in range(1, 31, 2): # odd k avoids ties in binary classification knn = KNeighborsClassifier(n_neighbors=k) scores.append(cross_val_score(knn, X_train, y_train, cv=5).mean())best_k = list(range(1, 31, 2))[np.argmax(scores)]
KNN Regression
Predict continuous targets by averaging neighbors.
python
from sklearn.neighbors import KNeighborsRegressorreg = KNeighborsRegressor(n_neighbors=10, weights='distance')reg.fit(X_train, y_train)preds = reg.predict(X_test) # weighted average of the k nearest neighbors' targets
Key Concepts
Core theory behind KNN.
- Lazy learning- KNN has no training phase; it stores the dataset and computes distances at prediction time
- Distance metric- Euclidean (default), Manhattan, or Minkowski distance defines what 'nearest' means
- n_neighbors (k)- Small k is sensitive to noise (overfitting); large k oversmooths (underfitting)
- weights='distance'- Weights closer neighbors more heavily than farther ones when voting or averaging
- Curse of dimensionality- Distance metrics grow less meaningful as feature count increases; reduce dimensions first if needed
Pro Tip
KNN's prediction cost scales with dataset size since it's a lazy learner with no training step — for large datasets, rely on sklearn's default algorithm='auto', which picks a KDTree or BallTree automatically, or use an approximate nearest-neighbor library like FAISS.
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