5. Visualizations

Scikit-learn defines a simple API for creating visualizations for machinelearning. The key feature of this API is to allow for quick plotting andvisual adjustments without recalculation. In the following example, we plot aROC curve for a fitted support vector machine:

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)

The returned svc_disp object allows us to continue using the already computedROC curve for SVC in future plots. In this case, the svc_disp is aRocCurveDisplay that stores the computed values asattributes called roc_auc, fpr, and tpr. Next, we train a random forestclassifier and plot the previously computed roc curve again by using the plotmethod of the Display object.

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)

Notice that we pass alpha=0.8 to the plot functions to adjust the alphavalues of the curves.

Examples:

• ROC Curve with Visualization API

• Advanced Plotting With Partial Dependence

5.1. Available Plotting Utilities

5.1.1. Functions

5.1.2. Display Objects