How to plot roc curve for random forest python

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)

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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.

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 svc_disp uses plot to plot the SVC ROC curve without recomputing the values of the roc curve itself. Furthermore, we pass alpha=0.8 to the plot functions to adjust the alpha values of the curves.

Total running time of the script: ( 0 minutes 0.142 seconds)

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