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plot_discretization_strategies.rst.txt
n_samples // 10, n_samples * 4 // 10, n_samples // 10, n_samples...n_samples * 4 // 10, ], cluster_std=0.5, centers=centers_0, random_state=random_state,...scikit-learn.org/stable/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt -
cross_validation.rst.txt
12 13 14] [ 2 3 10 15 16 17] [ 1 2 3 8 9 10 12 13 14 15 16 17]...size in question, then 5- or 10- fold cross validation can overestimate...scikit-learn.org/stable/_sources/modules/cross_validation.rst.txt -
feature_selection.rst.txt
selected features: if we have 10 features and ask for 7 selected...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
model_evaluation.rst.txt
10]}, ... scoring=ftwo_scorer, cv=5)...print(cv_results['test_tp']) [10 9 8 7 8] >>> # Getting the test...scikit-learn.org/stable/_sources/modules/model_evaluation.rst.txt -
linear_model.rst.txt
for example `cv=10` for 10-fold cross-validation, rather...computation 15.7 (2003): 1691-1714. <10.1162/089976603321891864>` |details-end|...scikit-learn.org/stable/_sources/modules/linear_model.rst.txt -
plot_multi_metric_evaluation.rst.txt
datasets import make_hastie_10_2 from sklearn.metrics import...code-block:: Python X, y = make_hastie_10_2(n_samples=8000, random_state=42)...scikit-learn.org/stable/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt -
grid_search.rst.txt
10, 100, 1000], 'kernel': ['linear']}, {'C': [1, 10, 100,...{'max_depth': [3, 5, 10], ... 'min_samples_split': [2, 5, 10]} >>> base_estimator...scikit-learn.org/stable/_sources/modules/grid_search.rst.txt -
getting_started.rst.txt
-10]] >>> # scale data according...5), ... 'max_depth': randint(5, 10)} ... >>> # now create a searchCV...scikit-learn.org/stable/_sources/getting_started.rst.txt -
preprocessing.rst.txt
asarray([["a"] * 20 + ["b"] * 10 + ["c"] * 10 + ["d"] * 10], dtype=object).T...ca.pdf>`_ Neural computation 10.5 (1998): 1299-1319. .. _pre...scikit-learn.org/stable/_sources/modules/preprocessing.rst.txt -
plot_release_highlights_1_4_0.rst.txt
sin(10 * np.pi * X[:, 0]) - noise rf_no_cst...e(7) groups = rng.randint(0, 10, size=n_samples) sample_weights...scikit-learn.org/stable/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.r...