1.Code
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_curve, roc_auc_score
from sklearn.model_selection import train_test_split
X, y = make_classification(n_samples=1000, n_features=10, n_classes=2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
classifiers = [
('Logistic Regression', LogisticRegression()),
('Decision Tree', DecisionTreeClassifier()),
('Random Forest', RandomForestClassifier())
]
for name, classifier in classifiers:
classifier.fit(X_train, y_train)
y_pred_proba = classifier.predict_proba(X_test)[:,1]
fpr, tpr, _ = roc_curve(y_test, y_pred_proba)
auc = roc_auc_score(y_test, y_pred_proba)
plt.plot(fpr, tpr, label=f'{
name} (AUC = {
auc:.2f})')
plt.plot([0, 1], [0, 1], 'k--', label='Random Guess')
plt.legend()
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.show()
2. Résultats
![insérez la description de l'image ici](https://img-blog.csdnimg.cn/218d3ce56f91470f990fec9ae574af7e.png)