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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

Confusion matrix

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

Precision-Recall

Receiver Operating Characteristic (ROC) with cross validation

Sample pipeline for text feature extraction and evaluation

Statistical comparison of models using grid search

Successive Halving Iterations

Test with permutations the significance of a classification score

Underfitting vs. Overfitting

Visualizing cross-validation behavior in scikit-learn

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https://scikit-learn.org/1.6/auto_examples/model_selection/index.html