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
Effect of model regularization on training and test error
Multiclass Receiver Operating Characteristic (ROC)
Nested versus non-nested cross-validation
Plotting Cross-Validated Predictions
Plotting Learning Curves and Checking Models’ Scalability
Post-hoc tuning the cut-off point of decision function
Post-tuning the decision threshold for cost-sensitive learning
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