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2.x 2027-03-01 15.1.x 2027-01-01...2024-02-24 14.3.x 2023-12-28 14.2.x 2023-10-26 14.1.x 2023-09-08...fess.codelibs.org/ja/eol.html -
homogeneity_score — scikit-learn 1.7.2 document...
2 ])) 1.000000 >>> print ( " %.6f...([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 2 , 3 ])) 1.000000 Clusters that...scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_score.html -
1.10. Decision Trees — scikit-learn 1.7.2 docum...
[ 2 , 2 ]] >>> y = [ 0.5 , 2.5 ] >>> clf = tree...samples: >>> clf . predict ([[ 2. , 2. ]]) array([1]) In case that...scikit-learn.org/stable/modules/tree.html -
GaussianProcessClassifier — scikit-learn 1.7.2 ...
2, and 5.1 from [RW2006] . Internally,...>>> gpc . predict_proba ( X [: 2 ,:]) array([[0.83548752, 0.03228706,...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html -
load_breast_cancer — scikit-learn 1.7.2 documen...
Classes 2 Samples per class 212(M),357(B)...target_names ndarray of shape (2,) The names of target classes....scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html -
PCA — scikit-learn 1.7.2 documentation
[ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2 , 1 ], [ 3...3 , 2 ]]) >>> pca = PCA ( n_components = 2 ) >>> pca . fit (...scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html -
log_loss — scikit-learn 1.7.2 documentation
2 ], [ .35 , .65 ]]) 0.21616 Gallery...scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html -
Hyperparameter — scikit-learn 1.7.2 documentation
bounds # Alias for field number 2 count ( value , / ) # Return number...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Hyperparameter.html -
ExtraTreesClassifier — scikit-learn 1.7.2 docum...
min_samples_split = 2 , min_samples_leaf = 1 , min_...min_samples_split int or float, default=2 The minimum number of samples...scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html -
Target Encoder’s Internal Cross fitting — sciki...
The TargetEncoder replaces each category of a categorical feature with the shrunk mean of the target variable for that category. This method is useful in cases where there is a strong relationship ...scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder_cross_val.html