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Model selection with Probabilistic PCA and Fact...
return pca_scores , fa_scores def shrunk_cov_score ( X ): shrinkages...Noise" )]: pca_scores , fa_scores = compute_scores ( X ) n_components_pca...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html -
4. Metadata Routing — scikit-learn 1.7.0 docume...
make_scorer ( accuracy_score ) . set_score_request ( sample_weight...make_scorer ( accuracy_score ) . set_score_request ( sample_weight...scikit-learn.org/stable/metadata_routing.html -
Plot the decision surfaces of ensembles of tree...
95 score RandomForestClassifi () # 0.94 score AdaBoost...0.94 score DecisionTree ( max_depth = None ) # 0.94 score Increasing...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_iris.html -
LogisticRegressionCV — scikit-learn 1.7.0 docum...
scoring str or callable, default=None The scoring method...confidence scores for samples. The confidence score for a sample...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html -
Comparing Random Forests and Histogram Gradient...
"Train time vs score" , "Predict time vs score" ], ) model_names...cv_results , x = "mean_score_time" , y = "mean_test_score" , error_x =...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html -
cross_validation.rst.txt
>>> scores = cross_val_score(clf, X, y, cv=5) >>> scores array([0.96,...>>> scores = cross_val_score( ... clf, X, y, cv=5, scoring='f1_macro')...scikit-learn.org/stable/_sources/modules/cross_validation.rst.txt -
HistGradientBoostingRegressor — scikit-learn 1....
train_score_ ndarray, shape (n_iter_+1,) The scores at each...iteration. Scores are computed according to the scoring parameter....scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html -
Non-negative least squares — scikit-learn 1.7.0...
( "NNLS R2 score" , r2_score_nnls ) NNLS R2 score 0.8225220806196525...print ( "OLS R2 score" , r2_score_ols ) OLS R2 score 0.7436926291700354...scikit-learn.org/stable/auto_examples/linear_model/plot_nnls.html -
L1 Penalty and Sparsity in Logistic Regression ...
69% Score with L1 penalty: 0.90 Score with Elastic-Net...penalty: 4.69% Score with L1 penalty: 0.90 Score with Elastic-Net...scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html -
2.3. Clustering — scikit-learn 1.7.0 documentation
concept as scores homogeneity_score and completeness_score . Both...Furthermore, both rand_score and adjusted_rand_score are symmetric :...scikit-learn.org/stable/modules/clustering.html