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TfidfVectorizer — scikit-learn 1.8.0 docu...
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 -
Polynomial and Spline interpolation — sci...
n_samples rows and degree + 1 columns: [[ 1 , x_0 , x_0 ** 2 , x_0...( X_plot )) axes [ 1 ] . legend ( axes [ 1 ] . lines , [ f "spline...scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html -
Wikipedia principal eigenvector — scikit-...
1 )[ 1 ] page_links_url = "h.... rsplit ( "/" , 1 )[ 1 ] resources = [ ( redirects_url...scikit-learn.org/stable/auto_examples/applications/wikipedia_principal_eigenvector.html -
SVR — scikit-learn 1.8.0 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 -
GaussianNB — scikit-learn 1.8.0 documenta...
([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2...2 , 1 ], [ 3 , 2 ]]) >>> Y = np . array ([ 1 , 1 , 1...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html -
Ridge — scikit-learn 1.8.0 documentation
Ridge ( alpha = 1.0 , * , fit_intercept = True ,...shape (n_targets,)}, default=1.0 Constant that multiplies the...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html -
mutual_info_regression — scikit-learn 1.8...
means 1 unless in a joblib.parallel_backend context. -1 means...References [ 1 ] Mutual Information on Wikipedia. [ 2 ] ( 1 , 2 ) A....scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_regression.html -
mutual_info_classif — scikit-learn 1.8.0 ...
means 1 unless in a joblib.parallel_backend context. -1 means...References [ 1 ] Mutual Information on Wikipedia. [ 2 ] ( 1 , 2 ) A....scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_classif.html -
k_means — scikit-learn 1.8.0 documentation
[ 1., 2.]]) >>> label array([1, 1, 1, 0, 0, 0],...X = np . array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 10 , 2...scikit-learn.org/stable/modules/generated/sklearn.cluster.k_means.html -
MultiTaskElasticNet — scikit-learn 1.8.0 ...
1 ) >>> clf . fit ([[ 0 , 0 ], [ 1 , 1 ], [ 2...], [ 1 , 1 ], [ 2 , 2 ]]) MultiTaskElasticNet(alpha=0.1) >>>...scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskElasticNet.html