Track 2 · Path · 8 explainers
Model Evaluation
Validation, cross-validation, the bias–variance tradeoff, and pitfalls
- 1
Model Evaluation & Metrics
Precision, recall, F1, ROC curves, and regression metrics
- 2
Train, Validation & Test Sets
Splitting data to measure generalization and avoid overfitting
- 3
Cross-Validation
K-fold, stratified, and leave-one-out evaluation
- 4
Bias-Variance Tradeoff
Why models underfit or overfit — the fundamental decomposition
- 5
Learning Curves
Diagnosing underfitting and overfitting from training size
- 6
Class Imbalance & Resampling
SMOTE, class weights, and metrics beyond accuracy
- 7
Feature Selection
Filter, wrapper, and embedded methods for choosing features
- 8
Curse of Dimensionality
Why high-dimensional spaces break intuition and algorithms