.. Places parent toc into the sidebar :parenttoc: True .. include:: includes/big_toc_css.rst .. _visualizations: ============== Visualizations ============== Scikit-learn defines a simple API for creating visualizations for machine learning. The key feature of this API is to allow for quick plotting and visual adjustments without recalculation. We provide `Display` classes that expose two methods for creating plots: `from_estimator` and `from_predictions`. The `from_estimator` method will take a fitted estimator and some data (`X` and `y`) and create a `Display` object. Sometimes, we would like to only compute the predictions once and one should use `from_predictions` instead. In the following example, we plot a ROC curve for a fitted support vector machine: .. plot:: :context: close-figs :align: center from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import RocCurveDisplay from sklearn.datasets import load_wine X, y = load_wine(return_X_y=True) y = y == 2 # make binary X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) svc = SVC(random_state=42) svc.fit(X_train, y_train) svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test) The returned `svc_disp` object allows us to continue using the already computed ROC curve for SVC in future plots. In this case, the `svc_disp` is a :class:`~sklearn.metrics.RocCurveDisplay` that stores the computed values as attributes called `roc_auc`, `fpr`, and `tpr`. Be aware that we could get the predictions from the support vector machine and then use `from_predictions` instead of `from_estimator`. Next, we train a random forest classifier and plot the previously computed roc curve again by using the `plot` method of the `Display` object. .. plot:: :context: close-figs :align: center import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifier rfc = RandomForestClassifier(n_estimators=10, random_state=42) rfc.fit(X_train, y_train) ax = plt.gca() rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=ax, alpha=0.8) svc_disp.plot(ax=ax, alpha=0.8) Notice that we pass `alpha=0.8` to the plot functions to adjust the alpha values of the curves. .. topic:: Examples: * :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py` * :ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py` * :ref:`sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py` * :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py` Available Plotting Utilities ============================ Display Objects --------------- .. currentmodule:: sklearn .. autosummary:: calibration.CalibrationDisplay inspection.PartialDependenceDisplay inspection.DecisionBoundaryDisplay metrics.ConfusionMatrixDisplay metrics.DetCurveDisplay metrics.PrecisionRecallDisplay metrics.PredictionErrorDisplay metrics.RocCurveDisplay model_selection.LearningCurveDisplay model_selection.ValidationCurveDisplay