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SelectFromModel — scikit-learn 1.7.2 documentation
- 1.34 , 0.31 ], ... [ - 2.79 , - 0.02 , - 0.85 ], ... [ - 1.34...1.34 , - 0.48 , - 2.55 ], ... [ 1.92 , 1.48 , 0.65 ]] >>> y =...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html -
plot_classifier_comparison.ipynb
x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n y_min,...GaussianProcessClass(1.0 * RBF(1.0), random_state=42),\n DecisionTreeClassifi(max_depth=5,...scikit-learn.org/stable/_downloads/3438aba177365cb595921cf18806dfa7/plot_classifier_comparison.ipynb -
GaussianNB — scikit-learn 1.7.2 documentation
- 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2 , 1...1 ], [ 3 , 2 ]]) >>> Y = np . array ([ 1 , 1 , 1 , 2 , 2 , 2...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html -
TweedieRegressor — scikit-learn 1.7.2 documenta...
intercept ). link {‘auto’, ‘identity’, ‘log’}, default=’auto’ The link...np.float64(0.839) >>> clf . coef_ array([0.599, 0.299]) >>> clf...scikit-learn.org/stable/modules/generated/sklearn.linear_model.TweedieRegressor.html -
Perceptron — scikit-learn 1.7.2 documentation
0.001 , shuffle = True , verbose = 0 , eta0 = 1.0 , n_jobs =...penalty {‘l2’,’l1’,’elasticnet’}, default=None The penalty (aka regularization...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html -
PolynomialFeatures — scikit-learn 1.7.2 documen...
) array([[ 1., 0., 1., 0.], [ 1., 2., 3., 6.], [ 1., 4., 5.,...1., 2., 3., 4., 6., 9.], [ 1., 4., 5., 16., 20., 25.]]) >>> poly...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html -
RANSACRegressor — scikit-learn 1.7.2 documentation
https://www.sri.com/wp-content/uploads/2021/12/ransac-publication.pdf...estimators. min_samples int (>= 1) or float ([0, 1]), default=None...scikit-learn.org/stable/modules/generated/sklearn.linear_model.RANSACRegressor.html -
GammaRegressor — scikit-learn 1.7.2 documentation
float64(0.773) >>> clf . coef_ array([0.073, 0.067]) >>> clf . intercept_...intercept_ np.float64(2.896) >>> clf . predict ([[ 1 , 0 ], [ 2 , 8 ]])...scikit-learn.org/stable/modules/generated/sklearn.linear_model.GammaRegressor.html -
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 -
MultiTaskLasso — scikit-learn 1.7.2 documentation
], [ 1 , 2 ], [ 2 , 4 ]], [[ 0 , 0 ], [ 1 , 1 ], [ 2 , 3 ]]) ...MultiTaskLasso(alpha=0.1) >>> print ( clf . coef_ ) [[0. 0.60809415]...scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskLasso.html