Index Symbols | _ | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X | Y Symbols 1d 1d array 2d 2d array _ _estimator_type A aggregate_over_ntiles() (scikitplot.modelplotpy.ModelPlotPy method) API array-like attribute attributes B backwards compatibility binary binary_ks_curve() (in module scikitplot.utils.helpers) C callable categorical feature check_input() (in module scikitplot.modelplotpy) class_weight classes_ classifier classifiers clone cloned clusterer clusterers coef_ combine_and_save_figures() (in module scikitplot.utils.helpers) common tests components_ consumer continuous continuous multi-output continuous multioutput cross fitting cross validation cross-fitting cross-validation cross-validation estimator cross-validation generator cross-validation splitter cumulative_gain_curve() (in module scikitplot.utils.helpers) cv CV splitter D data leakage data type decile_table() (in module scikitplot.kds) decision_function density estimator deprecation dimensionality docstring double underscore double underscore notation dtype duck typing E early stopping embedding_ estimator estimator instance estimator tags estimators evaluation metric evaluation metrics examples experimental F feature feature extractor feature extractors feature vector feature_importances_ features fit fit_predict fit_transform fitted fitting function G gallery get_feature_names_out get_n_splits get_params get_params() (scikitplot.modelplotpy.ModelPlotPy method) groups H hyper-parameter hyperparameter I imputation impute indexable induction inductive J joblib K kernel L label indicator matrix labels_ leakage M max_iter memmapping memory memory map memory mapping meta-estimator meta-estimators metadata metaestimator metaestimators metric missing values ModelPlotPy (class in scikitplot.modelplotpy) module scikitplot scikitplot.classifiers scikitplot.cluster scikitplot.clustering scikitplot.deciles scikitplot.decomposition scikitplot.estimators scikitplot.kds scikitplot.metrics scikitplot.modelplotpy scikitplot.plotters scikitplot.utils scikitplot.utils.helpers multi-class multi-class multi-output multi-label multi-output multi-output continuous multi-output multi-class multiclass multiclass multioutput multilabel multilabel indicator matrices multilabel indicator matrix multioutput multioutput continuous multioutput multiclass N n_components n_features n_iter_ n_iter_no_change n_jobs n_outputs n_samples n_targets narrative docs narrative documentation np O online learning out-of-core outlier detector outlier detectors outputs P pair pairwise metric pairwise metrics param parameter parameters params partial_fit pd plot_all() (in module scikitplot.modelplotpy) plot_calibration_curve() (in module scikitplot.metrics) plot_classifier_eval() (in module scikitplot.metrics) plot_confusion_matrix() (in module scikitplot.metrics) plot_costsrevs() (in module scikitplot.modelplotpy) plot_cumgains() (in module scikitplot.modelplotpy) plot_cumlift() (in module scikitplot.modelplotpy) plot_cumresponse() (in module scikitplot.modelplotpy) plot_cumulative_gain() (in module scikitplot.deciles) (in module scikitplot.kds) plot_elbow() (in module scikitplot.cluster) plot_feature_importances() (in module scikitplot.estimators) plot_ks_statistic() (in module scikitplot.deciles) (in module scikitplot.kds) plot_learning_curve() (in module scikitplot.estimators) plot_lift() (in module scikitplot.deciles) (in module scikitplot.kds) plot_lift_decile_wise() (in module scikitplot.kds) plot_pca_2d_projection() (in module scikitplot.decomposition) plot_pca_component_variance() (in module scikitplot.decomposition) plot_precision_recall() (in module scikitplot.metrics) plot_precision_recall_curve() (in module scikitplot.metrics) plot_profit() (in module scikitplot.modelplotpy) plot_response() (in module scikitplot.modelplotpy) plot_roc() (in module scikitplot.metrics) plot_roc_curve() (in module scikitplot.metrics) plot_roi() (in module scikitplot.modelplotpy) plot_silhouette() (in module scikitplot.metrics) plotting_scope() (scikitplot.modelplotpy.ModelPlotPy method) pos_label precomputed predict predict_log_proba predict_proba predictor predictors prepare_scores_and_ntiles() (scikitplot.modelplotpy.ModelPlotPy method) print_labels() (in module scikitplot.kds) R random_state range01() (in module scikitplot.modelplotpy) rectangular regressor regressors report() (in module scikitplot.kds) reset_modules() (scikitplot.modelplotpy.ModelPlotPy method) reset_params() (scikitplot.modelplotpy.ModelPlotPy method) router S sample sample properties sample property sample_weight samples scikit-learn enhancement proposals scikit-learn-contrib scikitplot module scikitplot.classifiers module scikitplot.cluster module scikitplot.clustering module scikitplot.deciles module scikitplot.decomposition module scikitplot.estimators module scikitplot.kds module scikitplot.metrics module scikitplot.modelplotpy module scikitplot.plotters module scikitplot.utils module scikitplot.utils.helpers module score score_samples scorer scoring semi-supervised semi-supervised learning semisupervised set_params set_params() (scikitplot.modelplotpy.ModelPlotPy method) show_versions() (in module scikitplot) sigmoid() (in module scikitplot.utils.helpers) SLEP SLEPs softmax() (in module scikitplot.utils.helpers) sparse graph sparse matrix split stateless supervised supervised learning T target targets transduction transductive transform transformer transformers U unlabeled unlabeled data unsupervised unsupervised learning V validate_labels() (in module scikitplot.utils.helpers) vectorizer vectorizers verbose W warm_start X X Xt Y Y y