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Prediction Intervals for Gradient Boosting Regr...
f ( xx ), "black" , linewidth = 3 , label = r "$f(x) = x\,\sin(x)$"...xx , f ( xx ), "black" , linewidth = 3 , label = r "$f(x) = x\,\sin(x)$"...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html -
Examples of Using FrozenEstimator — scikit-lear...
estimates for three data points: \n " f " { classifier . predict_proba...same three data points: \n " f " { classifier . predict ( X_test...scikit-learn.org/stable/auto_examples/frozen/plot_frozen_examples.html -
Multiclass Receiver Operating Characteristic (R...
name = f " { class_of_interest } vs the...effect of numpy.ravel : print ( f "y_score: \n { y_score [ 0 : 2...scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html -
Polynomial and Spline interpolation — scikit-le...
def f ( x ): """Function to be approximated...replace = False )) y_train = f ( x_train ) # create 2D-array...scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html -
LeaveOneGroupOut — scikit-learn 1.7.2 documenta...
print ( f "Fold { i } :" ) ... print ( f " Train: index=...train_index ] } " ) ... print ( f " Test: index= { test_index }...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneGroupOut.html -
Comparing Random Forests and Histogram Gradient...
print ( f "The dataset consists of { n_samples...only_physical_cores = True ) print ( f "Number of physical cores: { N_CORES...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html -
dump_svmlight_file — scikit-learn 1.7.2 documen...
data will be written to f. f should be opened in binary mode....dump_svmlight_file ( X , y , f , * , zero_based = True , comment...scikit-learn.org/stable/modules/generated/sklearn.datasets.dump_svmlight_file.html -
1.13. Feature selection — scikit-learn 1.7.2 do...
f_classif , mutual_info_classif The methods based on F-test...estimator. For instance, we can use a F-test to retrieve the two best...scikit-learn.org/stable/modules/feature_selection.html -
The Johnson-Lindenstrauss bound for embedding w...
legend ([ f "eps = { eps : 0.1f } " for eps...color = color ) plt . legend ([ f "n_samples = { n } " for n in...scikit-learn.org/stable/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html -
Plot classification probability — scikit-learn ...
set_title ( f "Class { label } " ) # plot data...0.16 , 0.015 ]) plt . title ( f "Probability class { label } "...scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html