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OrthogonalMatchingPursuitCV — scikit-learn 1.7....
1 ,]) array([-78.3854]) fit ( X , y , ** fit_params ) [source]...= 5 ) . fit ( X , y ) >>> reg . score ( X , y ) 0.9991 >>> reg...scikit-learn.org/stable/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html -
MultiTaskLasso — scikit-learn 1.7.1 documentation
], [ 1 , 2 ], [ 2 , 4 ]], [[ 0 , 0 ], [ 1 , 1 ], [ 2 , 3 ]]) ...MultiTaskLasso(alpha=0.1) >>> print ( clf . coef_ ) [[0. 0.60809415]...scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskLasso.html -
PolynomialFeatures — scikit-learn 1.7.1 documen...
) array([[ 1., 0., 1., 0.], [ 1., 2., 3., 6.], [ 1., 4., 5.,...1., 2., 3., 4., 6., 9.], [ 1., 4., 5., 16., 20., 25.]]) >>> poly...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html -
RANSACRegressor — scikit-learn 1.7.1 documentation
https://www.sri.com/wp-content/uploads/2021/12/ransac-publication.pdf...estimators. min_samples int (>= 1) or float ([0, 1]), default=None...scikit-learn.org/stable/modules/generated/sklearn.linear_model.RANSACRegressor.html -
IterativeImputer — scikit-learn 1.7.1 documenta...
initial_strategy {‘mean’, ‘median’, ‘most_frequent’, ‘constant’}, default=’mean’...X_{t-1}))/max(abs(X[known_vals])) < tol , where X_t is X at iteration...scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html -
permutation_importance — scikit-learn 1.7.1 doc...
9 , 9 ],[ 1 , 9 , 9 ],[ 1 , 9 , 9 ], ... [ 0 , 9 , 9 ],[ 0 , 9...9 , 9 ],[ 0 , 9 , 9 ]] >>> y = [ 1 , 1 , 1 , 0 , 0 , 0 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.inspection.permutation_importance.html -
normalized_mutual_info_score — scikit-learn 1.7...
fo_score ([ 0 , 0 , 1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> norm...fo_score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 If classes...scikit-learn.org/stable/modules/generated/sklearn.metrics.normalized_mutual_info_score.html -
StratifiedKFold — scikit-learn 1.7.1 documentation
np . array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]]) >>>...enumerate ( skf . split ( X , y )): ... print ( f "Fold { i } :" ) ......scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html -
DetCurveDisplay — scikit-learn 1.7.1 documentation
, estimator_name = "SVC" ... ) >>> display . plot () <...> >>>...from_estimator ( ... clf , X_test , y_test ) <...> >>> plt . show () classmethod...scikit-learn.org/stable/modules/generated/sklearn.metrics.DetCurveDisplay.html -
ConfusionMatrixDisplay — scikit-learn 1.7.1 doc...
classes_ ) >>> disp . plot () <...> >>> plt . show () classmethod...Sample weights. normalize {‘true’, ‘pred’, ‘all’}, default=None...scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html