.. 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. In the following example, we plot a ROC curve for a fitted support vector machine: .. code-block:: python from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import plot_roc_curve from sklearn.datasets import load_wine 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 = plot_roc_curve(svc, X_test, y_test) .. figure:: auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_001.png :target: auto_examples/miscellaneous/plot_roc_curve_visualization_api.html :align: center :scale: 75% 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`. Next, we train a random forest classifier and plot the previously computed roc curve again by using the `plot` method of the `Display` object. .. code-block:: python import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifier rfc = RandomForestClassifier(random_state=42) rfc.fit(X_train, y_train) ax = plt.gca() rfc_disp = plot_roc_curve(rfc, X_test, y_test, ax=ax, alpha=0.8) svc_disp.plot(ax=ax, alpha=0.8) .. figure:: auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_002.png :target: auto_examples/miscellaneous/plot_roc_curve_visualization_api.html :align: center :scale: 75% 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` Available Plotting Utilities ============================ Functions --------- .. currentmodule:: sklearn .. autosummary:: inspection.plot_partial_dependence metrics.plot_confusion_matrix metrics.plot_det_curve metrics.plot_precision_recall_curve metrics.plot_roc_curve Display Objects --------------- .. currentmodule:: sklearn .. autosummary:: inspection.PartialDependenceDisplay metrics.ConfusionMatrixDisplay metrics.DetCurveDisplay metrics.PrecisionRecallDisplay metrics.RocCurveDisplay