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SVR — scikit-learn 1.5.2 documentation
Added in version 1.1. n_support_ ndarray of shape (1,), dtype=int32..., tol = 0.001 , C = 1.0 , epsilon = 0.1 , shrinking = True ,...scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html -
MaxAbsScaler — scikit-learn 1.5.2 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 -
GaussianNB — scikit-learn 1.5.2 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 -
IsolationForest — scikit-learn 1.5.2 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 -
StratifiedKFold — scikit-learn 1.5.2 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 -
TfidfVectorizer — scikit-learn 1.5.2 documentation
ngram_range=(1 , 1) , max_df=1.0 , min_df=1 , max_features=None...ngram_range of (1, 1) means only unigrams, (1, 2) means unigrams...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html -
BernoulliRBM — scikit-learn 1.5.2 documentation
1 , 1 ], [ 1 , 0 , 1 ], [ 1 , 1 , 1 ]]) >>> model...= 256 , * , learning_rate = 0.1 , batch_size = 10 , n_iter = 10...scikit-learn.org/stable/modules/generated/sklearn.neural_network.BernoulliRBM.html -
cluster_optics_dbscan — scikit-learn 1.5.2 docu...
1, 1, 1]) Gallery examples # Demo of...compute_optics_graph >>> X = np . array ([[ 1 , 2 ], [ 2 , 5 ], [ 3 , 6 ], ......scikit-learn.org/stable/modules/generated/sklearn.cluster.cluster_optics_dbscan.html -
cross_validate — scikit-learn 1.5.2 documentation
means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn 1.4 Release Highlights for scikit-learn 1.4 Categorical...scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html -
RANSACRegressor — scikit-learn 1.5.2 documentation
min_samples int (>= 1) or float ([0, 1]), default=None Minimum...min_samples is chosen as X.shape[1] + 1 . This parameter is highly...scikit-learn.org/stable/modules/generated/sklearn.linear_model.RANSACRegressor.html