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load_breast_cancer — scikit-learn 1.7.2 documen...
load_breast_cancer () >>> data . target [[ 10 , 50 , 85 ]] array([0, 1, 0]) >>>...list ( data . target_names ) [np.str_('malignant'), np.str_('benign')]...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html -
fetch_kddcup99 — scikit-learn 1.7.2 documentation
Guide . Added in version 0.18. Parameters : subset {‘SA’, ‘SF’,...encountered. Added in version 1.5. delay float, default=1.0 Number...scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_kddcup99.html -
make_swiss_roll — scikit-learn 1.7.2 documentation
make_swiss_roll ( noise = 0.05 , random_state = 0 ) >>> X . shape (100, 3) >>>...sklearn.datasets. make_swiss_roll ( n_samples = 100 , * , noise = 0.0 , random_state...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_swiss_roll.html -
LassoLarsIC — scikit-learn 1.7.2 documentation
], [ - 1 , 1 ], [ 0 , 0 ], [ 1 , 1 ], [ 2 , 2 ]] >>> y = [ - 2.2222...2.2222 , - 1.1111 , 0 , - 1.1111 , - 2.2222 ] >>> reg . fit (...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsIC.html -
log_loss — scikit-learn 1.7.2 documentation
log_loss ([ "spam" , "ham" , "ham" , "spam" ], ... [[ .1 , .9 ], [...[ .9 , .1 ], [ .8 , .2 ], [ .35 , .65 ]]) 0.21616 Gallery examples...scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html -
RidgeCV — scikit-learn 1.7.2 documentation
linear_model. RidgeCV ( alphas = (0.1, 1.0, 10.0) , * , fit_intercept...array-like of shape (n_alphas,), default=(0.1, 1.0, 10.0) Array...scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html -
QuantileRegressor — scikit-learn 1.7.2 document...
intercept. solver {‘highs-ds’, ‘highs-ipm’, ‘highs’, ‘interior-point’,...<= reg . predict ( X )) np.float64(0.8) fit ( X , y , sample_weight...scikit-learn.org/stable/modules/generated/sklearn.linear_model.QuantileRegressor.html -
mean_tweedie_deviance — scikit-learn 1.7.2 docu...
= [ 2 , 0 , 1 , 4 ] >>> y_pred = [ 0.5 , 0.5 , 2. , 2. ] >>>...mean_tweedie_deviance ( y_true , y_pred , power = 1 ) 1.4260... Gallery examples...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_tweedie_deviance.html -
roc_curve — scikit-learn 1.7.2 documentation
, 0. , 0.5, 0.5, 1. ]) >>> tpr array([0. , 0.5, 0.5, 1. , 1....1. ]) >>> thresholds array([ inf, 0.8 , 0.4 , 0.35, 0.1 ]) Gallery...scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html -
det_curve — scikit-learn 1.7.2 documentation
array([0. , 0.5, 0.5]) >>> thresholds array([0.35, 0.4 , 0.8 ])...([ 0.1 , 0.4 , 0.35 , 0.8 ]) >>> fpr , fnr , thresholds = det_curve...scikit-learn.org/stable/modules/generated/sklearn.metrics.det_curve.html