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Isotonic Regression — scikit-learn 1.8.0 docume...
versionadded:: 1.7 1e-06 n_jobs n_jobs: int, default=None...that is if firstly `n_targets > 1` and secondly `X` is sparse or...scikit-learn.org/stable/auto_examples/miscellaneous/plot_isotonic_regression.html -
RadiusNeighborsTransformer — scikit-learn 1.8.0...
() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) set_output...who’s the closest point to [1, 1, 1]: >>> import numpy as np >>>...scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsTransformer.html -
ExtraTreeClassifier — scikit-learn 1.8.0 docume...
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead...instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The “balanced” mode uses...scikit-learn.org/stable/modules/generated/sklearn.tree.ExtraTreeClassifier.html -
permutation_importance — scikit-learn 1.8.0 doc...
permutation_importance >>> X = [[ 1 , 9 , 9 ],[ 1 , 9 , 9 ],[ 1 , 9 , 9 ], ... [...],[ 0 , 9 , 9 ]] >>> y = [ 1 , 1 , 1 , 0 , 0 , 0 ] >>> clf = LogisticRegression...scikit-learn.org/stable/modules/generated/sklearn.inspection.permutation_importance.html -
check_X_y — scikit-learn 1.8.0 documentation
ensure_min_samples = 1 , ensure_min_features = 1 , y_numeric = False...>>> X = [[ 1 , 2 ], [ 3 , 4 ], [ 5 , 6 ]] >>> y = [ 1 , 2 , 3 ]...scikit-learn.org/stable/modules/generated/sklearn.utils.check_X_y.html -
non_negative_factorization — scikit-learn 1.8.0...
array ([[ 1 , 1 ], [ 2 , 1 ], [ 3 , 1.2 ], [ 4 , 1 ], [ 5 , 0.8...version 1.4: Added 'auto' value. Changed in version 1.6: Default...scikit-learn.org/stable/modules/generated/sklearn.decomposition.non_negative_factorization.html -
auc — scikit-learn 1.8.0 documentation
array ([ 1 , 1 , 2 , 2 ]) >>> y_score = np . array ([ 0.1 , 0.4...scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html -
MultiTaskElasticNetCV — scikit-learn 1.8.0 docu...
[ 1 , 1 ], [ 2 , 2 ]], ... [[ 0 , 0 ], [ 1 , 1 ], [ 2 ,...with 0 < l1_ratio <= 1. For l1_ratio = 1 the penalty is an L1/L2...scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.html -
ClassNamePrefixFeaturesOutMixin — scikit-learn ...
shape [ 1 ] ... return self >>> X = np . array ([[ 1 , 2 ], [...scikit-learn.org/stable/modules/generated/sklearn.base.ClassNamePrefixFeaturesOutMixin.html -
RidgeCV — scikit-learn 1.8.0 documentation
1, 1.0, 10.0) , * , fit_intercept...shape (n_alphas,), default=(0.1, 1.0, 10.0) Array of alpha values...scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html