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
Effect of model regularization on training and test error
Multiclass Receiver Operating Characteristic (ROC)
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