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ROC Curve with Visualization API¶
Scikit-learn defines a simple API for creating visualizations for machine learning. The key features of this API is to allow for quick plotting and visual adjustments without recalculation. In this example, we will demonstrate how to use the visualization API by comparing ROC curves.
Load Data and Train a SVC¶
First, we load the wine dataset and convert it to a binary classification problem. Then, we train a support vector classifier on a training dataset.
import matplotlib.pyplot as plt from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import RocCurveDisplay 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(random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Plotting the ROC Curve¶
Next, we plot the ROC curve with a single call to
sklearn.metrics.RocCurveDisplay.from_estimator. The returned
svc_disp object allows us to continue using the already computed ROC curve
for the SVC in future plots.
svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test) plt.show()
Training a Random Forest and Plotting the ROC Curve¶
We train a random forest classifier and create a plot comparing it to the SVC
ROC curve. Notice how
plot to plot the SVC ROC curve
without recomputing the values of the roc curve itself. Furthermore, we
alpha=0.8 to the plot functions to adjust the alpha values of the
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) plt.show()
Total running time of the script: ( 0 minutes 0.209 seconds)