- Sort Score
- Result 10 results
- Languages All
- Labels All
Results 1 - 10 of 48 for * (1.68 sec)
-
cross_validation.rst.txt
((90, 4), (90,)) >>> X_test.shape, y_test.shape ((60, 4), (60,))...cross_val_score(clf, X, y, cv=5) >>> scores array([0.96, 1. , 0.96, 0.96,...scikit-learn.org/stable/_sources/modules/cross_validation.rst.txt -
plot_discretization_strategies.rst.txt
meshgrid( np.linspace(X[:, 0].min(), X[:, 0].max(), 300), np.linspace(X[:,...len(strategies) + 1, i) ax.scatter(X[:, 0], X[:, 1], edgecolors="k") if ds_cnt...scikit-learn.org/stable/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt -
feature_selection.rst.txt
= [[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0,....8))) >>> sel.fit_transform(X) array([[0, 1], [1, 0], [0, 0], [1,...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
model_evaluation.rst.txt
evaluation (`mean_pinball_loss(..., alpha=0.99)` - we apologize...Association 102 (2007), pp. 359– 378. `link to pdf <https://sites.sta...scikit-learn.org/stable/_sources/modules/model_evaluation.rst.txt -
plot_multi_metric_evaluation.rst.txt
for sample, style in (("train", "--"), ("test", "-")): sample_score_mean...plt.ylabel("Score") ax = plt.gca() ax.set_xlim(0, 402) ax.set_ylim(0.73,...scikit-learn.org/stable/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt -
governance.rst.txt
way, they are a contributor. Core Contributors ---------- All...relevant to their roles. Contributors ---------- Contributors are community...scikit-learn.org/stable/_sources/governance.rst.txt -
linear_model.rst.txt
linear_model.Ridge(alpha=.5) >>> reg.fit([[0, 0], [0, 0], [1, 1]], [0,...value. .. math:: \hat{y}(w, x) = w_0 + w_1 x_1 + ... + w_p x_p...scikit-learn.org/stable/_sources/modules/linear_model.rst.txt -
about.rst.txt
utors>`__. Active Core Contributors ---------- Maintainers Team...<contributing>`. Documentation Team .......... The following people...scikit-learn.org/stable/_sources/about.rst.txt -
grid_search.rst.txt
[1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},...= [ {'C': [1, 10, 100, 1000], 'kernel': ['linear']}, {'C': [1,...scikit-learn.org/stable/_sources/modules/grid_search.rst.txt -
preprocessing.rst.txt
= np.array([[ 1., -1., 2.], ... [ 2., 0., 0.], ... [ 0., 1., -1.]])...1.33 ], [ 1.22, 0. , -0.267], [-1.22, 1.22, -1.06 ]]) .. >>> import...scikit-learn.org/stable/_sources/modules/preprocessing.rst.txt