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make_sparse_coded_signal — scikit-learn 1.7.1 d...
= 50 , ... n_components = 100 , ... n_features = 10 , ... n_nonzero_coefs...= 4 , ... random_state = 0 ... ) >>> data . shape (50, 10) >>>...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_sparse_coded_signal.html -
make_s_curve — scikit-learn 1.7.1 documentation
X . shape (100, 3) >>> t . shape (100,) Gallery examples # Comparison...sklearn.datasets. make_s_curve ( n_samples = 100 , * , noise = 0.0 , random_state...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_s_curve.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 -
TweedieRegressor — scikit-learn 1.7.1 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 -
PolynomialFeatures — scikit-learn 1.7.1 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 -
Perceptron — scikit-learn 1.7.1 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 -
3.5. Validation curves: plotting scores to eval...
0.9 , 0.96, 0.9 ], [0.9, 0.83, 0.96, 0.96, 0.93], [1. , 0.93,...0.94, 0.91, 0.89, 0.92], [0.9 , 0.92, 0.93, 0.92, 0.93], [0.97, 1...scikit-learn.org/stable/modules/learning_curve.html -
9.2. Computational Performance — scikit-learn 1...
or one-at-a-time mode. 9.2.1.1. Bulk versus Atomic mode # In general...sparsity_ratio ( X ): return 1.0 - np . count_nonzero ( X ) / float ( X ....scikit-learn.org/stable/computing/computational_performance.html -
MultiTaskLasso — scikit-learn 1.7.1 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 -
HuberRegressor — scikit-learn 1.7.1 documentation
HuberRegressor () . fit ( X , y ) >>> huber . score ( X , y ) -7.284 >>>...coef ) True coefficients: [20.4923... 34.1698...] >>> print ( "Huber...scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html