Visualizations

• Scikit-learn visualization API examples

Example: ROC curve from SVC fit

• svc_disp object enables reusing the ROC curve in future plots.

import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import plot_roc_curve
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split

X, y = load_wine(return_X_y=True)
y = y == 2

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)

Train Random Forest, plot against existing SVC ROC Curve

• note: svc_disp can re-plot the SVC ROC curve without recalculating it.

rfc = RandomForestClassifier(n_estimators=10, 
                             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)

<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f7518176c10>