- Sort Score
- Result 10 results
- Languages All
- Labels All
Results 1441 - 1450 of 4,657 for * (3.12 sec)
-
GroupShuffleSplit — scikit-learn 1.7.1 document...
= ( 8 , 1 )) >>> groups = np . array ([ 1 , 1 , 2 , 2 , 2 , 3...gss . split ( X , y , groups )): ... print ( f "Fold { i } :" )...scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupShuffleSplit.html -
GroupKFold — scikit-learn 1.7.1 documentation
, 10 ], [ 11 , 12 ]]) >>> y = np . array ([ 1 , 2 , 3 , 4 , 5...5 , 6 ]) >>> groups = np . array ([ 0 , 0 , 2 , 2 , 3 , 3 ]) >>>...scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupKFold.html -
WhiteKernel — scikit-learn 1.7.1 documentation
True ) (array([653.0, 592.1 ]), array([316.6, 316.6])) __call__...noise_level_bounds = (1e-05, 100000.0) ) [source] # White kernel. The main...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.WhiteKernel.html -
GaussianProcessRegressor — scikit-learn 1.7.1 d...
constant_value_bounds="fixed") * RBF(1.0, length_scale_bounds="fixed") is used...None , * , alpha = 1e-10 , optimizer = 'fmin_l_bfgs_b' , n_restarts_optimizer...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html -
PassiveAggressiveClassifier — scikit-learn 1.7....
n_samples / (n_classes * np.bincount(y)) . Added in version 0.17: parameter...sklearn.linear_model. PassiveAggressiveCla ( * , C = 1.0 , fit_intercept...scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html -
ExpSineSquared — scikit-learn 1.7.1 documentation
True ) (array([425.6, 457.5]), array([0.3894, 0.3467])) __call__...100000.0) , periodicity_bounds = (1e-05, 100000.0) ) [source] #...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ExpSineSquared.html -
Lasso — scikit-learn 1.7.1 documentation
linear_model . Lasso ( alpha = 0.1 ) >>> clf . fit ([[ 0 , 0 ], [ 1 , 1...is: ( 1 / ( 2 * n_samples )) * || y - Xw ||^ 2_2 + alpha * ||...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html -
VotingRegressor — scikit-learn 1.7.1 documentation
]) >>> er = VotingRegressor ([( 'lr' , r1 ), ( 'rf' , r2 ), (...array ([[ 1 , 1 ], [ 2 , 4 ], [ 3 , 9 ], [ 4 , 16 ], [ 5 , 25 ], [...scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingRegressor.html -
lasso_path — scikit-learn 1.7.1 documentation
, 2 , 3.1 ], [ 2.3 , 5.4 , 4.3 ]]) . T >>> y = np . array ([...1. , .5 ]) >>> print ( coef_path ) [[0. 0. 0.46874778] [0.2159048...scikit-learn.org/stable/modules/generated/sklearn.linear_model.lasso_path.html -
Kernel — scikit-learn 1.7.1 documentation
diag ( self , X ): ... return np . ones ( X . shape [ 0 ]) ... def...self , X , Y = None ): ... if Y is None : ... Y = X ... return...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Kernel.html