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make_regression — scikit-learn 1.5.2 documentation
n_features = 2 , noise = 1 , random_state = 42...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html -
StackingClassifier — scikit-learn 1.5.2 documen...
2 (1992): 241-259. Examples >>>...(n_samples, n_classes * (n_classes-1) / 2) The decision function computed...scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html -
MLPClassifier — scikit-learn 1.5.2 documentation
beta_2 = 0.999 , epsilon = 1e-08 , n_iter_no_change...array-like of shape(n_layers - 2,), default=(100,) The ith element...scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html -
LearningCurveDisplay — scikit-learn 1.5.2 docum...
2 Release Highlights for scikit-learn 1.2 Comparison...visualization. Added in version 1.2. Parameters : train_sizes ndarray...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LearningCurveDisplay.html -
1.15. Isotonic regression — scikit-learn 1.5.2 ...
\sum_i w_i (y_i - \hat{y}_i)^2\] subject to \(\hat{y}_i \le \hat{y}_j\)...scikit-learn.org/stable/modules/isotonic.html -
1.16. Probability calibration — scikit-learn 1....
reliability diagrams (Wilks 1995 [ 2 ] ), compare how well the probabilistic...in this dataset which contains 2 redundant features. RandomForestClassifi...scikit-learn.org/stable/modules/calibration.html -
4.1. Partial Dependence and Individual Conditio...
2 , ( 3 , 2 )] >>> PartialDependenceDis...results [ "average" ] array([[ 2.466..., 2.466..., ... >>> results [...scikit-learn.org/stable/modules/partial_dependence.html -
pairwise_distances — scikit-learn 1.5.2 documen...
2.], [2., 1.]]) Gallery examples #...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html -
check_is_fitted — scikit-learn 1.5.2 documentation
2 ], [ 1 , 3 ]], [ 1 , 0 ]) LogisticRegression()...scikit-learn.org/stable/modules/generated/sklearn.utils.validation.check_is_fitted.html -
LeavePGroupsOut — scikit-learn 1.5.2 documentation
group=[2] Test: index=[0 2], group=[1 3] Fold 2: Train: index=[0],...array ([ 1 , 2 , 1 ]) >>> groups = np . array ([ 1 , 2 , 3 ]) >>>...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePGroupsOut.html