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Classifier comparison — scikit-learn 1.8.0 docu...
n_features = 2 , n_redundant = 0 , n_informative = 2 , random_state.... random . RandomState ( 2 ) X += 2 * rng . uniform ( size =...scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html -
NuSVC — scikit-learn 1.8.0 documentation
[ - 2 , - 1 ], [ 1 , 1 ], [ 2 , 1 ]]) >>> y = np...np . array ([ 1 , 1 , 2 , 2 ]) >>> from sklearn.pipeline import...scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html -
Detection error tradeoff (DET) curve — scikit-l...
n_features = 2 , n_redundant = 0 , n_informative = 2 , random_state...ax_det ] = plt . subplots ( 1 , 2 , figsize = ( 11 , 5 )) ax_roc...scikit-learn.org/stable/auto_examples/model_selection/plot_det.html -
test.rst
第一項目 - Lorem ipsum 2. 第二項目 - 吾輩は猫である 3. 第三項目 - Test...dolor sit amet | +----+---- | 2 | 吾輩は猫である | 夏目漱石の小説 | +----+----...raw.githubusercontent.com/codelibs/fess-testdata/master/files/markdown/test.rst -
check_increasing — scikit-learn 1.8.0 documenta...
2 , 3 , 4 , 5 ], [ 2 , 4 , 6 , 8 , 10 ] >>>...np.True_ >>> y = [ 10 , 8 , 6 , 4 , 2 ] >>> check_increasing ( x , y...scikit-learn.org/stable/modules/generated/sklearn.isotonic.check_increasing.html -
Permutation Importance vs Random Forest Feature...
result in up to `max_categories + 2` integer codes. .. versionadded::...instead. .. versionadded:: 1.2 False RandomForestClassifi ? Documentation...scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance.html -
pydata-sphinx-theme.css
g-2,.gx-2{--bs-gutter-x:0.5rem}.g-2,.gy-2{--bs-gutte....g-sm-2,.gx-sm-2{--bs-gutter-x:0.5rem}.g-sm-2,.gy-sm-2{--bs-...scikit-learn.org/stable/_static/styles/pydata-sphinx-theme.css -
SGDRegressor — scikit-learn 1.8.0 documentation
loss/||x||**2) . ‘pa2’: passive-aggressive algorithm 2, see [1]...eta = hinge_loss / (||x||**2 + 1/(2 eta0)) . Added in version...scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html -
clustering.rst.txt
2, 0, 3, 4, 5, 1] >>> labels_pred = [1, 1, 0, 0, 2, 2, 2,...1, 2, 0, 3, 4, 5, 1] >>> labels_pred = [1, 1, 0, 0, 2, 2, 2,...scikit-learn.org/stable/_sources/modules/clustering.rst.txt -
RBF — scikit-learn 1.8.0 documentation
x_j)^2}{2l^2} \right)\] where \(l\) is the...very smooth. See [2] , Chapter 4, Section 4.2, for further details...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RBF.html