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sklearn.isotonic — scikit-learn 1.7.1 documenta...
Isotonic regression for obtaining monotonic fit to data. User guide. See the Isotonic regression section for further details.scikit-learn.org/stable/api/sklearn.isotonic.html -
sklearn.impute — scikit-learn 1.7.1 documentation
Transformers for missing value imputation. User guide. See the Imputation of missing values section for further details.scikit-learn.org/stable/api/sklearn.impute.html -
randomized_svd — scikit-learn 1.7.1 documentation
approximation problem described in [1] (problem (1.5), p5). Refer to Wikipedia...n_iter=0 or 1 should even work fine in theory (see [1] page 9)....scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.randomized_svd.html -
coverage_error — scikit-learn 1.7.1 documentation
y_true = [[ 1 , 0 , 0 ], [ 0 , 1 , 1 ]] >>> y_score = [[ 1 , 0 , 0...[ 0 , 1 , 1 ]] >>> coverage_error ( y_true , y_score ) 1.5 On...scikit-learn.org/stable/modules/generated/sklearn.metrics.coverage_error.html -
make_friedman1 — scikit-learn 1.7.1 documentation
Annals of Statistics 19 (1), pages 1-67, 1991. [ 2 ] L. Breiman,...[source] # Generate the “Friedman #1” regression problem. This dataset...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman1.html -
Feature agglomeration — scikit-learn 1.7.1 docu...
- 1 )) connectivity = grid_to_graph...images . shape ) plt . figure ( 1 , figsize = ( 4 , 3.5 )) plt ....scikit-learn.org/stable/auto_examples/cluster/plot_digits_agglomeration.html -
Gradient Boosting regression — scikit-learn 1.7...
subplot ( 1 , 1 , 1 ) plt . title ( "Deviance"...12 , 6 )) plt . subplot ( 1 , 2 , 1 ) plt . barh ( pos , feature_importance...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html -
log_loss — scikit-learn 1.7.1 documentation
p) = -(y \log (p) + (1 - y) \log (1 - p))\] Read more in the..., "spam" ], ... [[ .1 , .9 ], [ .9 , .1 ], [ .8 , .2 ], [ .35...scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html -
dcg_score — scikit-learn 1.7.1 documentation
asarray ([[ 1 , 0 , 0 , 0 , 1 ]]) >>> # by default ties...to have a score between 0 and 1. References Wikipedia entry for...scikit-learn.org/stable/modules/generated/sklearn.metrics.dcg_score.html -
power_transform — scikit-learn 1.7.1 documentation
'box-cox' )) [[-1.332 -0.707] [ 0.256 -0.707] [ 1.076 1.414]] Warning...Available methods are: ‘yeo-johnson’ [1] , works with positive and negative...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.power_transform.html