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5.1. Partial Dependence and Individual Conditio...
1.1. Partial dependence plots # Partial..., learning_rate = 1.0 , ... max_depth = 1 , random_state = 0...scikit-learn.org/stable/modules/partial_dependence.html -
f_classif — scikit-learn 1.8.0 documentation
n_clusters_per_class = 1 , ... shuffle = False , random_state...f_statistic array([2.21e+02, 7.02e-01, 1.70e+00, 9.31e-01, 5.41e+00, 3.25e-01,...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_classif.html -
ValidationCurveDisplay — scikit-learn 1.8.0 doc...
means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn 1.3 Release Highlights for scikit-learn 1.3 On this...scikit-learn.org/stable/modules/generated/sklearn.model_selection.ValidationCurveDisplay.html -
export_text — scikit-learn 1.8.0 documentation
(cm) <= 1.75 | | |--- class: 1 | |--- petal width (cm) > 1.75 |...decision_tree.classes_ . Added in version 1.3. max_depth int, default=10 Only...scikit-learn.org/stable/modules/generated/sklearn.tree.export_text.html -
Displaying Pipelines — scikit-learn 1.8.0 docum...
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead...instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The "balanced" mode uses...scikit-learn.org/stable/auto_examples/miscellaneous/plot_pipeline_display.html -
Kernel PCA — scikit-learn 1.8.0 documentation
1 ], c = y_train ) train_ax . set_ylabel ( "Feature #1" )...( X_test [:, 0 ], X_test [:, 1 ], c = y_test ) test_ax . set_xlabel...scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html -
Isotonic Regression — scikit-learn 1.8.0 docume...
versionadded:: 1.7 1e-06 n_jobs n_jobs: int, default=None...that is if firstly `n_targets > 1` and secondly `X` is sparse or...scikit-learn.org/stable/auto_examples/miscellaneous/plot_isotonic_regression.html -
GaussianRandomProjection — scikit-learn 1.8.0 d...
Added in version 1.1. n_features_in_ int Number of...n_components = 'auto' , * , eps = 0.1 , compute_inverse_components =...scikit-learn.org/stable/modules/generated/sklearn.random_projection.GaussianRandomProjection.html -
SelectFpr — scikit-learn 1.8.0 documentation
Added in version 1.0. See also f_classif ANOVA F-value..."x1", ..., "x(n_features_in_ - 1)"] . If input_features is an array-like,...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFpr.html -
1.11. Ensembles: Gradient boosting, random fore...
Gradient Boosting models 1.11.1.1.1. Usage # Most of the parameters...= [[ 1 , 0 ], ... [ 1 , 0 ], ... [ 1 , 0 ], ... [ 0 , 1 ]] >>>...scikit-learn.org/stable/modules/ensemble.html