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plot_classifier_comparison.rst.txt
and test part X, y = ds X_train, X_test, y_train, y_test = train_test_split(...Plot the testing points ax.scatter( X_test[:, 0], X_test[:, 1],...scikit-learn.org/stable/_sources/auto_examples/classification/plot_classifier_comparison.rst.txt -
plot_multi_metric_evaluation.rst.txt
``'_<scorer_name>'`` (``'mean_test_precision'``, ``'rank_test_precision'``, etc...)...style in (("train", "--"), ("test", "-")): sample_score_mean =...scikit-learn.org/stable/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt -
plot_release_highlights_1_4_0.rst.txt
X_test, y_train, y_test = train_test_split(X_adult,...--sklearn-color-text-on-default-background: var(--sg-text-color, va...scikit-learn.org/stable/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.r... -
test.cpp
#include <sdtio.h> int main(void) { printf("Lorem ipsum. (ロレム・イプサム) 吾輩は猫である。"); }raw.githubusercontent.com/codelibs/fess-testdata/master/source_code/test.cpp -
feature_selection.rst.txt
based on univariate statistical tests. It can be seen as a preprocessing...common univariate statistical tests for each feature: false positive...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
preprocessing.rst.txt
K_{test} - 1'_{\text{n}_{samples}} K - K_{test} 1_{\text{n}_{samples}}...>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)...scikit-learn.org/stable/_sources/modules/preprocessing.rst.txt -
plot_discretization_strategies.rst.txt
.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/preprocessing/plot_discretization_strategies.py" .. LI...scikit-learn.org/stable/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt -
test.js
function foo() { var bar = "Lorem ipsum. (ロレム・イプサム) 吾輩は猫である。"; }raw.githubusercontent.com/codelibs/fess-testdata/master/source_code/test.js -
support.rst.txt
about repository updates and test failures on the `scikit-learn-commits...scikit-learn.org/stable/_sources/support.rst.txt -
clustering.rst.txt
math:: \text{ARI} = \frac{\text{RI} - E[\text{RI}]}{\max(\text{RI})...math:: \text{AMI} = \frac{\text{MI} - E[\text{MI}]}{\text{mean}(H(U),...scikit-learn.org/stable/_sources/modules/clustering.rst.txt