# 5. 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:

from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import RocCurveDisplay

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 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.

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.

## 5.1. Available Plotting Utilities¶

### 5.1.1. Display Objects¶

 calibration.CalibrationDisplay(prob_true, ...) Calibration curve (also known as reliability diagram) visualization. inspection.PartialDependenceDisplay(...[, ...]) Partial Dependence Plot (PDP). inspection.DecisionBoundaryDisplay(*, xx0, ...) Decisions boundary visualization. metrics.ConfusionMatrixDisplay(...[, ...]) Confusion Matrix visualization. metrics.DetCurveDisplay(*, fpr, fnr[, ...]) DET curve visualization. metrics.PrecisionRecallDisplay(precision, ...) Precision Recall visualization. metrics.PredictionErrorDisplay(*, y_true, y_pred) Visualization of the prediction error of a regression model. metrics.RocCurveDisplay(*, fpr, tpr[, ...]) ROC Curve visualization. Learning Curve visualization.