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mean_pinball_loss — scikit-learn 1.5.2 document...
2 , 3 ] >>> mean_pinball_loss ( y_true , [ 0 , 2 , 3 ],...mean_pinball_loss ( y_true , [ 1 , 2 , 4 ], alpha = 0.1 ) np.float64(0.3...)...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_pinball_loss.html -
1.10. Decision Trees — scikit-learn 1.5.2 docum...
[ 2 , 2 ]] >>> y = [ 0.5 , 2.5 ] >>> clf = tree...samples: >>> clf . predict ([[ 2. , 2. ]]) array([1]) In case that...scikit-learn.org/stable/modules/tree.html -
silhouette_samples — scikit-learn 1.5.2 documen...
defined if number of labels is 2 <= n_labels <= n_samples - 1 ....Applied Mathematics 20: 53-65. [ 2 ] Wikipedia entry on the Silhouette...scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_samples.html -
log_loss — scikit-learn 1.5.2 documentation
2 ], [ .35 , .65 ]]) 0.21616......scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html -
HistGradientBoostingClassifier — scikit-learn 1...
split on features 2, 3 and 4. Added in version 1.2. warm_start bool,...version 0.24. Changed in version 1.2: Added support for feature names....scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.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 -
affinity_propagation — scikit-learn 1.5.2 docum...
2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2 ], [ 4 , 4...scikit-learn.org/stable/modules/generated/sklearn.cluster.affinity_propagation.html -
HuberRegressor — scikit-learn 1.5.2 documentation
float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...penalty is equal to alpha * ||w||^2 . Must be in the range [0, inf)...scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html -
RegressorChain — scikit-learn 1.5.2 documentation
2 ], [ 1 , 1 ], [ 2 , 0 ]] >>> chain = RegressorChain...predict ( X ) array([[0., 2.], [1., 1.], [2., 0.]]) fit ( X , Y ,...scikit-learn.org/stable/modules/generated/sklearn.multioutput.RegressorChain.html -
PLSSVD — scikit-learn 1.5.2 documentation
[ 2. , 2. , 2. ], ... [ 2. , 5. , 4. ]]) >>>...([[ 0.1 , - 0.2 ], ... [ 0.9 , 1.1 ], ... [ 6.2 , 5.9 ], ... [...scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSSVD.html