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ValidationCurveDisplay — scikit-learn 1.5.2 doc...
means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn 1.3 Release Highlights for scikit-learn 1.3 Plotting...scikit-learn.org/stable/modules/generated/sklearn.model_selection.ValidationCurveDisplay.html -
Release Highlights for scikit-learn 1.5 — sciki...
array([[-1.1, 1.1, 1.1], [ 3.9, -1.2, 1.1], [ 0.1, 1.3, 1.1], [-0.1,...[-0.1, -1.4, -1.4], [-4.9, 1.5, -1.5], [ 0.1, 1.6, 1.6]]) Pairwise...scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html -
log_loss — scikit-learn 1.5.2 documentation
p) = -(y \log (p) + (1 - y) \log (1 - p))\] Read more in the..., "spam" ], ... [[ .1 , .9 ], [ .9 , .1 ], [ .8 , .2 ], [ .35...scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html -
Sparsity Example: Fitting only features 1 and 2...
1 , - 0.1 ], [ 0.15 , 0.15 ]]), np . array ([[ - 0.1 , 0.15...0.1 , 0.15 , 0.15 ], [ - 0.1 , 0.15 , - 0.1 , 0.15 ]]) . T )...scikit-learn.org/stable/auto_examples/linear_model/plot_ols_3d.html -
make_circles — scikit-learn 1.5.2 documentation
int64(1), np.int64(1), np.int64(1), np.int64(0), np.int64(0)]...outer circle in the range [0, 1) . Returns : X ndarray of shape...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html -
Bunch — scikit-learn 1.5.2 documentation
import Bunch >>> b = Bunch ( a = 1 , b = 2 ) >>> b [ 'b' ] 2 >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.Bunch.html -
precision_score — scikit-learn 1.5.2 documentation
[ 1 , 1 , 1 ], [ 0 , 1 , 1 ]] >>> y_pred = [[...[[ 0 , 0 , 0 ], [ 1 , 1 , 1 ], [ 1 , 1 , 0 ]] >>> precision_score...scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html -
completeness_score — scikit-learn 1.5.2 documen...
1 , 1 ], [ 1 , 1 , 0 , 0 ]) np.float64(1.0) Non-perfect...completeness_score ([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 0 , 1 ])) 0.0 >>> print...scikit-learn.org/stable/modules/generated/sklearn.metrics.completeness_score.html -
f1_score — scikit-learn 1.5.2 documentation
[ 1 , 1 , 1 ], [ 0 , 1 , 1 ]] >>> y_pred = [[...[[ 0 , 0 , 0 ], [ 1 , 1 , 1 ], [ 1 , 1 , 0 ]] >>> f1_score ( y_true...scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html -
1.17. Neural network models (supervised) — scik...
[ 1. , 1. ]] >>> y = [[ 0 , 1 ], [ 1 , 1 ]] >>> clf...= [[ 0. , 0. ], [ 1. , 1. ]] >>> y = [ 0 , 1 ] >>> clf = MLPClassifier...scikit-learn.org/stable/modules/neural_networks_supervised.html