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homogeneity_score — scikit-learn 1.7.2 document...
2 ])) 1.000000 >>> print ( " %.6f...([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 2 , 3 ])) 1.000000 Clusters that...scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_score.html -
make_scorer — scikit-learn 1.7.2 documentation
beta = 2 ) >>> ftwo_scorer make_scorer(fbeta_score,...response_method='predict', beta=2) >>> from sklearn.model_selection...scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html -
RFE — scikit-learn 1.7.2 documentation
2, 5]) decision_function ( X ) [source]...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html -
1.10. Decision Trees — scikit-learn 1.7.2 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 -
log_loss — scikit-learn 1.7.2 documentation
2 ], [ .35 , .65 ]]) 0.21616 Gallery...scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html -
ComplementNB — scikit-learn 1.7.2 documentation
2. Changed in version 1.4: The default...)) >>> y = np . array ([ 1 , 2 , 3 , 4 , 5 , 6 ]) >>> from sklearn.naive_bayes...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.ComplementNB.html -
Shield Reference for 2.x and 1.x | Elastic
for 2.x and 1.x: 2.3 Shield Reference for 2.x and 1.x: 2.2 Shield...for 2.x and 1.x: 2.1 Shield Reference for 2.x and 1.x: 2.0 Shield...www.elastic.co/guide/en/shield/index.html -
make_friedman2 — scikit-learn 1.7.2 documentation
0 ] ** 2 + ( X [:, 1 ] * X [:, 2 ] - 1 / ( X [:, 1...[source] # Generate the “Friedman #2” regression problem. This dataset...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman2.html -
mean_absolute_error — scikit-learn 1.7.2 docume...
2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>> mean_absolute_error...]] >>> y_pred = [[ 0 , 2 ], [ - 1 , 2 ], [ 8 , - 5 ]] >>> mean_absolute_error...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html -
mean_squared_error — scikit-learn 1.7.2 documen...
2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>> mean_squared_error...]] >>> y_pred = [[ 0 , 2 ],[ - 1 , 2 ],[ 8 , - 5 ]] >>> mean_squared_error...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html