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PLSSVD — scikit-learn 1.8.0 documentation
1 , - 0.2 ], ... [ 0.9 , 1.1 ], ... [ 6.2 , 5.9...np . array ([[ 0. , 0. , 1. ], ... [ 1. , 0. , 0. ], ... [ 2. ,...scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSSVD.html -
median_absolute_error — scikit-learn 1.8.0 docu...
1 ], [ - 1 , 1 ], [ 7 , - 6 ]] >>> y_pred...y_pred = [[ 0 , 2 ], [ - 1 , 2 ], [ 8 , - 5 ]] >>> median_absolute_error...scikit-learn.org/stable/modules/generated/sklearn.metrics.median_absolute_error.html -
k_means — scikit-learn 1.8.0 documentation
[ 1., 2.]]) >>> label array([1, 1, 1, 0, 0, 0], dtype=int32)...X = np . array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 10 , 2...scikit-learn.org/stable/modules/generated/sklearn.cluster.k_means.html -
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 -
MultiOutputClassifier — scikit-learn 1.8.0 docu...
( X [ - 2 :]) array([[1, 1, 1], [1, 0, 1]]) fit ( X , Y , sample_weight...means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputClassifier.html -
IsolationForest — scikit-learn 1.8.0 documentation
1 ], [ 0 ], [ 90 ]]) array([ 1, 1, -1]) For an example...from 0.1 to 'auto' . max_features int or float, default=1.0 The...scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html -
partial_dependence — scikit-learn 1.8.0 documen...
[ 1 , 0 , 0 ]] >>> y = [ 0 , 1 ] >>> from sklearn.ensemble...interacting features (e.g. [(0, 1)] ) for which the partial dependency...scikit-learn.org/stable/modules/generated/sklearn.inspection.partial_dependence.html -
det_curve — scikit-learn 1.8.0 documentation
y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise...labels are not either {-1, 1} or {0, 1}, then pos_label should...scikit-learn.org/stable/modules/generated/sklearn.metrics.det_curve.html -
StratifiedKFold — scikit-learn 1.8.0 documentation
array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]])...>>> y = np . array ([ 0 , 0 , 1 , 1 ]) >>> skf = StratifiedKFold...scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html -
nan_euclidean_distances — scikit-learn 1.8.0 do...
6] and [1, na, 4, 5] is: \[\sqrt{\frac{4}{2}((3-1)^2 + (6-5)^2)}\]...float ( "NaN" ) >>> X = [[ 0 , 1 ], [ 1 , nan ]] >>> nan_euclidean_distances...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html