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LeaveOneOut — scikit-learn 1.6.1 documentation
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.LeaveOneOut.html -
PredefinedSplit — scikit-learn 1.6.1 documentation
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.PredefinedSplit.html -
Custom refit strategy of a grid search with cro...
target == 8 print ( f "The number of images is { X ....filtered_cv_results [ "params" ], ): print ( f "precision: { mean_precision :...scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_digits.html -
Prediction Intervals for Gradient Boosting Regr...
f ( xx ), "g:" , linewidth = 3 , label = r "$f(x) = x\,\sin(x)$"...( xx , f ( xx ), "g:" , linewidth = 3 , label = r "$f(x) = x\,\sin(x)$"...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.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 -
Gaussian processes on discrete data structures ...
baseline_similarity_bounds ) def _f ( self , s1 , s2 ): """ kernel...return ( np . array ([[ self . _f ( x , y ) for y in Y ] for x in...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_on_structured_data.html -
LeavePGroupsOut — scikit-learn 1.6.1 documentation
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.LeavePGroupsOut.html -
Combine predictors using stacking — scikit-lear...
{ key : ( f " { np . abs ( np . mean ( scores [ f 'test_ { value...])) : .2f } +- " f " { np . std ( scores [ f 'test_ { value }...scikit-learn.org/stable/auto_examples/ensemble/plot_stack_predictors.html -
fbeta_score — scikit-learn 1.6.1 documentation
[source] # Compute the F-beta score. The F-beta score is the weighted...precision. The formula for F-beta score is: \[F_\beta = \frac{(1 + \beta^2)...scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html -
Illustration of prior and posterior Gaussian pr...
label = f "Sampled function # { idx + 1...plt . tight_layout () print ( f "Kernel parameters before fit:...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_prior_posterior.html