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MACHINE LEARNING

Model Evaluation

Complete model evaluation toolkit: bias-variance decomposition with bootstrap, learning curves, k-fold cross-validation with per-fold scores, and permutation feature importance — model-agnostic.

Bias-VarLearningCurvek-FoldPerm
OPEN INTERACTIVE LAB ↗

What you'll explore

  • Model evaluation
  • Bias variance tradeoff
  • K-fold cross validation
  • Learning curve
  • Feature importance
  • Ml metrics

About this lab

Complete model evaluation toolkit: bias-variance decomposition with bootstrap, learning curves, k-fold cross-validation with per-fold scores, and permutation feature importance — model-agnostic. This simulation runs entirely in your browser — no installation, no account required, no data uploaded.

Part of the Machine Learning Labs track — 5 labs covering the full curriculum.

PLATFORM FEATURES
Runs 100% in browser — no server, no installs
Adjustable parameters with real-time output
Privacy-first: zero data collection or uploads
Blockchain-verifiable experiment logs on Polygon
Free to use — open to everyone