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