Model Selection#
Examples related to the sklearn.model_selection
module.
![](../../_images/sphx_glr_plot_grid_search_refit_callable_thumb.png)
Balance model complexity and cross-validated score
![](../../_images/sphx_glr_plot_likelihood_ratios_thumb.png)
Class Likelihood Ratios to measure classification performance
![](../../_images/sphx_glr_plot_randomized_search_thumb.png)
Comparing randomized search and grid search for hyperparameter estimation
![](../../_images/sphx_glr_plot_successive_halving_heatmap_thumb.png)
Comparison between grid search and successive halving
![](../../_images/sphx_glr_plot_grid_search_digits_thumb.png)
Custom refit strategy of a grid search with cross-validation
![](../../_images/sphx_glr_plot_multi_metric_evaluation_thumb.png)
Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV
![](../../_images/sphx_glr_plot_roc_thumb.png)
Multiclass Receiver Operating Characteristic (ROC)
![](../../_images/sphx_glr_plot_learning_curve_thumb.png)
Plotting Learning Curves and Checking Models’ Scalability
![](../../_images/sphx_glr_plot_tuned_decision_threshold_thumb.png)
Post-hoc tuning the cut-off point of decision function
![](../../_images/sphx_glr_plot_cost_sensitive_learning_thumb.png)
Post-tuning the decision threshold for cost-sensitive learning
![](../../_images/sphx_glr_plot_roc_crossval_thumb.png)
Receiver Operating Characteristic (ROC) with cross validation
![](../../_images/sphx_glr_plot_grid_search_text_feature_extraction_thumb.png)
Sample pipeline for text feature extraction and evaluation
![](../../_images/sphx_glr_plot_grid_search_stats_thumb.png)
Statistical comparison of models using grid search
![](../../_images/sphx_glr_plot_permutation_tests_for_classification_thumb.png)
Test with permutations the significance of a classification score
![](../../_images/sphx_glr_plot_cv_indices_thumb.png)
Visualizing cross-validation behavior in scikit-learn