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Getting Started — scikit-learn 1.8.0 documentation
dataset is easy array([1., 1., 1., 1., 1.]) Automatic parameter...transform ( X ) array([[-1., 1.], [ 1., -1.]]) Sometimes, you want...scikit-learn.org/stable/getting_started.html -
fetch_covtype — scikit-learn 1.8.0 documentation
Added in version 1.5. delay float, default=1.0 Number of seconds...False , n_retries = 3 , delay = 1.0 ) [source] # Load the covertype...scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_covtype.html -
randomized_range_finder — scikit-learn 1.8.0 do...
array ([[ 1 , 2 , 3 ], [ 4 , 5 , 6 ], [ 7...scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.randomized_range_finder.html -
FastICA — scikit-learn 1.8.0 documentation
The implementation is based on [1] . Read more in the User Guide...performed. Changed in version 1.3: The default value of whiten...scikit-learn.org/stable/modules/generated/sklearn.decomposition.FastICA.html -
TargetEncoder — scikit-learn 1.8.0 documentation
1 ] * 15 + [ 20.4 ] * 5 + [ 20.1 ] * 25 + [ 21.2...scikit-learn 1.3 Release Highlights for scikit-learn 1.3 On this...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html -
assert_all_finite — scikit-learn 1.8.0 document...
array ([ 1 , np . inf , np . nan , 4 ]) >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.assert_all_finite.html -
l1_min_c — scikit-learn 1.8.0 documentation
intercept_scaling = 1.0 ) [source] # Return the lowest...sticRegression with l1_ratio=1 . This value is valid if class_weight...scikit-learn.org/stable/modules/generated/sklearn.svm.l1_min_c.html -
make_moons — scikit-learn 1.8.0 documentation
) The integer labels (0 or 1) for class membership of each...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html -
Bunch — scikit-learn 1.8.0 documentation
import Bunch >>> b = Bunch ( a = 1 , b = 2 ) >>> b [ 'b' ] 2 >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.Bunch.html -
Kernel — scikit-learn 1.8.0 documentation
length_scale = 1.0 ): ... self . length_scale =...2.0 ) >>> X = np . array ([[ 1 , 2 ], [ 3 , 4 ]]) >>> print (...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Kernel.html