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FrozenEstimator — scikit-learn 1.8.0 documentation
random_state = 0 ) >>> clf = LogisticRegression ( random_state = 0 ) . fit...scikit-learn 1.6 Release Highlights for scikit-learn 1.6 On this...scikit-learn.org/stable/modules/generated/sklearn.frozen.FrozenEstimator.html -
dcg_score — scikit-learn 1.8.0 documentation
asarray ([[ 1 , 0 , 0 , 0 , 1 ]]) >>> # by default ties...true_relevance = np . asarray ([[ 10 , 0 , 0 , 1 , 5 ]]) >>> # we predict...scikit-learn.org/stable/modules/generated/sklearn.metrics.dcg_score.html -
log_loss — scikit-learn 1.8.0 documentation
1 , .9 ], [ .9 , .1 ], [ .8 , .2 ], [ .35 , .65 ]]) 0.21616...\[L_{\log}(y, p) = -(y \log (p) + (1 - y) \log (1 - p))\] Read more in the...scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html -
IsotonicRegression — scikit-learn 1.8.0 documen...
make_regression ( n_samples = 10 , n_features = 1 , random_state = 41 )...iso_reg . predict ([ .1 , .2 ]) array([1.8628, 3.7256]) fit (...scikit-learn.org/stable/modules/generated/sklearn.isotonic.IsotonicRegression.html -
Matern — scikit-learn 1.8.0 documentation
array([[0.8513, 0.0368, 0.1117], [0.8086, 0.0693, 0.1220]]) __call__...length_scale = 1.0 , length_scale_bounds = (1e-05, 100000.0) , nu = 1.5 )...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Matern.html -
classification_report — scikit-learn 1.8.0 docu...
67 0.80 3 2 0.00 0.00 0.00 0 3 0.00 0.00 0.00 0 micro avg 1.00...class 0 0.50 1.00 0.67 1 class 1 0.00 0.00 0.00 1 class 2 1.00 0.67...scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html -
roc_curve — scikit-learn 1.8.0 documentation
array([0. , 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...scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html -
Exponentiation — scikit-learn 1.8.0 documentation
noise = 0 , random_state = 0 ) >>> kernel = Exponentiation...random_state = 0 ) . fit ( X , y ) >>> gpr . score ( X , y ) 0.419 >>>...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Exponentiation.html -
SelectorMixin — scikit-learn 1.8.0 documentation
shape [ 1 ] ... return self ... def _get_support_mask..."x1", ..., "x(n_features_in_ - 1)"] . If input_features is an array-like,...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectorMixin.html -
matthews_corrcoef — scikit-learn 1.8.0 document...
[ + 1 , + 1 , + 1 , - 1 ] >>> y_pred = [ + 1 , - 1 , + 1 , +...value between -1 and +1. A coefficient of +1 represents a perfect...scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html