← Back to all experiments
Semi-Supervised LearningIntermediateKNNLabel Propagation

EXP5: Semi-Supervised KNN Labeling

Labels unlabeled data using KNN and compares accuracy across k values.

Key highlights

  • Generates synthetic labeled/unlabeled split
  • Evaluates multiple k values
  • Captures accuracy trends

Metrics

Accuracy per k

Dataset

Synthetic

↓ Scroll to Python codeView report

Outputs

Histogram output
Distribution of synthetic values

Full Python Code

The complete Python script directly from the repository.

Python
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
accuracy = accuracy_score(true_labels, predictions)

Next steps

Want to explore further? Try the full gallery or open the raw script to tweak parameters.

Back to galleryOpen code on GitHub