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GaussianNB — scikit-learn 1.6.0 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 -
LinearSVR — scikit-learn 1.6.0 documentation
intercept term: [x_1, ..., x_n, 1] , where 1 corresponds to the...version 1.3: The "auto" option is added in version 1.3 and will...scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVR.html -
MultiOutputClassifier — scikit-learn 1.6.0 docu...
( X [ - 2 :]) array([[1, 1, 1], [1, 0, 1]]) fit ( X , Y , sample_weight...means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputClassifier.html -
LeavePOut — scikit-learn 1.6.0 documentation
3] Test: index=[0 1] Fold 1: Train: index=[1 3] Test: index=[0...index=[1 2] Fold 4: Train: index=[0 2] Test: index=[1 3] Fold...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePOut.html -
SGD: Penalties — scikit-learn 1.6.0 documentation
[( - 1 , - 1 )], ) plt . clabel ( l2_contour , inline = 1 , fontsize...18 , fmt = { 1.0 : "L2" }, manual = [( - 1 , - 1 )]) plt . clabel...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_penalties.html -
MLPClassifier — scikit-learn 1.6.0 documentation
:]) array([1, 0, 1, 0, 1]) >>> clf . score ( X_test..., validation_fraction = 0.1 , beta_1 = 0.9 , beta_2 = 0.999 ,...scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html -
BaggingClassifier — scikit-learn 1.6.0 document...
max_samples = 1.0 , max_features = 1.0 , bootstrap = True...means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html -
matthews_corrcoef — scikit-learn 1.6.0 document...
[ + 1 , + 1 , + 1 , - 1 ] >>> y_pred = [ + 1 , - 1 , + 1 , +...value between -1 and +1. A coefficient of +1 represents a perfect...scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html -
Polynomial and Spline interpolation — scikit-le...
[ 1 , x_1 , x_1 ** 2 , x_1 ** 3 , ... , x_1 ** degree...), ... ], [ basis_1 ( x_1 ), basis_2 ( x_1 ), ... ], ... ] This...scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html -
polynomial_kernel — scikit-learn 1.7.dev0 docum...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>..., degree = 2 ) array([[1. , 1. ], [1.77..., 2.77...]]) On this...scikit-learn.org/dev/modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html