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Comparing Random Forests and Histogram Gradient...
row = 1 , col = 1 ) fig . add_trace ( line_trace , row = 1 , col...row = 1 , col = 2 ) fig . add_trace ( line_trace , row = 1 , col...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html -
Comparison of LDA and PCA 2D projection of Iris...
1 , 2 ], target_names ): plt . scatter...y == i , 0 ], X_r [ y == i , 1 ], color = color , alpha = 0.8...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html -
Detection error tradeoff (DET) curve — scikit-l...
random_state = 1 , n_clusters_per_class = 1 , ) X_train , X_test...make_classification ( n_samples = 1_000 , n_features = 2 , n_redundant...scikit-learn.org/stable/auto_examples/model_selection/plot_det.html -
get_routing_for_object — scikit-learn 1.7.2 doc...
Added in version 1.3. Parameters : obj object If...scikit-learn.org/stable/modules/generated/sklearn.utils.metadata_routing.get_routing_for_object.html -
Factor Analysis (with rotation) to visualize pa...
vmin =- 1 , vmax = 1 ) ax . set_xticks ([ 0 , 1 , 2 , 3 ])...set_xticks ([ 0 , 1 ]) ax . set_xticklabels ([ "Comp. 1" , "Comp. 2"...scikit-learn.org/stable/auto_examples/decomposition/plot_varimax_fa.html -
Non-negative least squares — scikit-learn 1.7.2...
In this example, we fit a linear model with positive constraints on the regression coefficients and compare the estimated coefficients to a classic linear regression. Generate some random data Spli...scikit-learn.org/stable/auto_examples/linear_model/plot_nnls.html -
sklearn.cross_decomposition — scikit-learn 1.7....
Algorithms for cross decomposition. User guide. See the Cross decomposition section for further details.scikit-learn.org/stable/api/sklearn.cross_decomposition.html -
sklearn.linear_model — scikit-learn 1.7.2 docum...
A variety of linear models. User guide. See the Linear Models section for further details. The following subsections are only rough guidelines: the same estimator can fall into multiple categories,...scikit-learn.org/stable/api/sklearn.linear_model.html -
sklearn.semi_supervised — scikit-learn 1.7.2 do...
Semi-supervised learning algorithms. These algorithms utilize small amounts of labeled data and large amounts of unlabeled data for classification tasks. User guide. See the Semi-supervised learnin...scikit-learn.org/stable/api/sklearn.semi_supervised.html -
get_scorer_names — scikit-learn 1.7.2 documenta...
Skip to main content Back to top Ctrl + K GitHub Choose version get_scorer_names # sklearn.metrics. get_scorer_names ...scikit-learn.org/stable/modules/generated/sklearn.metrics.get_scorer_names.html