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1.4. Support Vector Machines — scikit-learn 1.7...
2}\) \(\alpha^{2}_{0,2}\) \(\alpha^{0}_{1,2}\) \(\alpha^{1}_{1,2}\)...\(\alpha^{0}_{2,0}\) \(\alpha^{1}_{2,0}\) \(\alpha^{0}_{0,2}\) \(\alpha^{1}_{0,2}\)...scikit-learn.org/stable/modules/svm.html -
ledoit_wolf — scikit-learn 1.7.1 documentation
2 ], [ .2 , .8 ]]) >>> rng = np . random...scikit-learn.org/stable/modules/generated/sklearn.covariance.ledoit_wolf.html -
StratifiedShuffleSplit — scikit-learn 1.7.1 doc...
2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 ,...Test: index=[0 2 3] Fold 2: Train: index=[5 0 2] Test: index=[4...scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html -
plot_classifier_comparison.py
make_classification( n_features=2, n_redundant=0, n_informative=2, random_state=1,...rng = np.random.RandomState(2) X += 2 * rng.uniform(size=X.shape)...scikit-learn.org/stable/_downloads/2da0534ab0e0c8241033bcc2d912e419/plot_classifier_comparison.py -
IsotonicRegression — scikit-learn 1.7.1 documen...
float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...Operations Research Vol. 14, No. 2 (May, 1989), pp. 303-308 Isotone...scikit-learn.org/stable/modules/generated/sklearn.isotonic.IsotonicRegression.html -
QuantileRegressor — scikit-learn 1.7.1 document...
float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...n_samples , n_features = 10 , 2 >>> rng = np . random . RandomState...scikit-learn.org/stable/modules/generated/sklearn.linear_model.QuantileRegressor.html -
v_measure_score — scikit-learn 1.7.1 documentation
2 ], [ 0 , 0 , 1 , 1 ])) 0.8 >>>...% v_measure_score ([ 0 , 1 , 2 , 3 ], [ 0 , 0 , 1 , 1 ])) 0.67...scikit-learn.org/stable/modules/generated/sklearn.metrics.v_measure_score.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 -
LinearRegression — scikit-learn 1.7.1 documenta...
2 ], [ 2 , 2 ], [ 2 , 3 ]]) >>> # y = 1 * x_0 + 2 * x_1...float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html -
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