Model Selection¶
Examples related to the sklearn.model_selection
module.
Balance model complexity and cross-validated score
Class Likelihood Ratios to measure classification performance
Comparing randomized search and grid search for hyperparameter estimation
Comparison between grid search and successive halving
Custom refit strategy of a grid search with cross-validation
Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV
Detection error tradeoff (DET) curve
Multiclass Receiver Operating Characteristic (ROC)
Nested versus non-nested cross-validation
Plotting Cross-Validated Predictions
Plotting Learning Curves and Checking Models’ Scalability
Receiver Operating Characteristic (ROC) with cross validation
Sample pipeline for text feature extraction and evaluation
Statistical comparison of models using grid search
Test with permutations the significance of a classification score
Visualizing cross-validation behavior in scikit-learn