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RBFSampler — scikit-learn 1.5.0 documentation
[ 1 , 1 ], [ 1 , 0 ], [ 0 , 1 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>>...in version 1.2: The option "scale" was added in 1.2. n_components...scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.RBFSampler.html -
LatentDirichletAllocation — scikit-learn 1.5.0 ...
evaluate_every = -1 , total_samples = 1000000.0 , perp_tol = 0.1 , mean_change_tol...None, defaults to 1 / n_components . In [1] , this is called...scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html -
Lasso — scikit-learn 1.5.0 documentation
1 ) >>> clf . fit ([[ 0 , 0 ], [ 1 , 1 ], [ 2 , 2...* || W || _11 where \(||W||_{1,1}\) is the sum of the magnitude...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html -
1.11. Ensembles: Gradient boosting, random fore...
Gradient Boosting models 1.11.1.1.1. Usage # Most of the parameters...= [[ 1 , 0 ], ... [ 1 , 0 ], ... [ 1 , 0 ], ... [ 0 , 1 ]] >>>...scikit-learn.org/stable/modules/ensemble.html -
GaussianNB — scikit-learn 1.5.0 documentation
([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2...2 , 1 ], [ 3 , 2 ]]) >>> Y = np . array ([ 1 , 1 , 1 , 2 , 2...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html -
MaxAbsScaler — scikit-learn 1.5.0 documentation
-1. , 1. ], [ 1. , 0. , 0. ], [ 0. , 1. , -0.5]]) fit...= [[ 1. , - 1. , 2. ], ... [ 2. , 0. , 0. ], ... [ 0. , 1. , -...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html -
IsolationForest — scikit-learn 1.5.0 documentation
1 ], [ 0 ], [ 90 ]]) array([ 1, 1, -1]) For an example...from 0.1 to 'auto' . max_features int or float, default=1.0 The...scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html -
ColumnTransformer — scikit-learn 1.5.0 document...
1. , 2. , 2. ], ... [ 1. , 1. , 0. , 1. ]]) >>> #...scikit-learn 1.1 Release Highlights for scikit-learn 1.1 Release Highlights...scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html -
StandardScaler — scikit-learn 1.5.0 documentation
( data )) [[-1. -1.] [-1. -1.] [ 1. 1.] [ 1. 1.]] >>> print (...0 , 0 ], [ 0 , 0 ], [ 1 , 1 ], [ 1 , 1 ]] >>> scaler = StandardScaler...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html -
StratifiedKFold — scikit-learn 1.5.0 documentation
array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]])...>>> y = np . array ([ 0 , 0 , 1 , 1 ]) >>> skf = StratifiedKFold...scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html