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hinge_loss — scikit-learn 1.8.0 documentation
[ 1 ]] >>> y = [ - 1 , 1 ] >>> est = svm . LinearSVC...0.09]) >>> hinge_loss ([ - 1 , 1 , 1 ], pred_decision ) 0.30 In...scikit-learn.org/stable/modules/generated/sklearn.metrics.hinge_loss.html -
multilabel_confusion_matrix — scikit-learn 1.8....
array([[[1, 0], [0, 1]], [[1, 0], [0, 1]], [[0, 1], [1, 0]]]) Multiclass...array([[[3, 1], [0, 2]], [[5, 0], [1, 0]], [[2, 1], [1, 2]]]) On...scikit-learn.org/stable/modules/generated/sklearn.metrics.multilabel_confusion_matrix.html -
DBFlute : Migration : 0.8.1
1} 環境上の注意点 Sql2Entityも一緒に バージョン...C#ユーザの方へ 最新バージョンのS2Container-1.3.11と一緒にご利用下さい。 実装上の注意点 特になし...dbflute.seasar.org/ja/oldmigration/migrate-080to081.html -
GradientBoostingRegressor — scikit-learn 1.8.0 ...
1 , n_estimators = 100 , subsample = 1.0 , criterion...in the range [1, inf) . subsample float, default=1.0 The fraction...scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html -
RandomForestRegressor — scikit-learn 1.8.0 docu...
min_samples_leaf = 1 , min_weight_fraction_leaf = 0.0 , max_features = 1.0 ,...e.g. 0.3. Changed in version 1.1: The default of max_features...scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html -
robust_scale — scikit-learn 1.8.0 documentation
independently array([[-1., 1., 1.], [ 1., -1., -1.]]) >>> robust_scale...robust_scale >>> X = [[ - 2 , 1 , 2 ], [ - 1 , 0 , 1 ]] >>> robust_scale...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.robust_scale.html -
confusion_matrix_at_thresholds — scikit-learn 1...
1., 1., 0.]) >>> fps array([0., 1., 1., 2.]) >>> fns...fns array([1., 1., 0., 0.]) >>> tps array([1., 1., 2., 2.]) >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix_at_thresholds.html -
Perceptron — scikit-learn 1.8.0 documentation
l1_ratio <= 1 . l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1....means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html -
DictVectorizer — scikit-learn 1.8.0 documentation
[{ 'foo' : 1 , 'bar' : 2 }, { 'foo' : 3 , 'baz' : 1 }] >>> X =...) >>> X array([[2., 0., 1.], [0., 1., 3.]]) >>> v . inverse_transform...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html -
PCA — scikit-learn 1.8.0 documentation
([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2...more details. Added in version 1.1. power_iteration_normalizer {‘auto’,...scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html