How to plot ROC curve in Python?

The ROC (Receiver Operating Characteristic) curve is a graphical plot used to evaluate binary classification models. It shows the trade-off between true positive rate (sensitivity) and false positive rate (1-specificity) at various threshold settings.

Python's sklearn.metrics module provides the plot_roc_curve() method to easily visualize ROC curves for classification models.

Steps to Plot ROC Curve

• Generate a random binary classification dataset using make_classification() method

• Split the data into training and testing sets using train_test_split() method

• Train a classifier (like SVM) on the training data using fit() method

• Plot the ROC curve using plot_roc_curve() method

• Display the plot using plt.show() method

Example

Here's how to create and plot an ROC curve for a Support Vector Machine classifier ?

import matplotlib.pyplot as plt
from sklearn import datasets, metrics, model_selection, svm

# Generate binary classification dataset
X, y = datasets.make_classification(random_state=0)

# Split data into training and testing sets
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, random_state=0)

# Create and train SVM classifier
clf = svm.SVC(random_state=0)
clf.fit(X_train, y_train)

# Plot ROC curve
metrics.plot_roc_curve(clf, X_test, y_test)
plt.show()

Understanding ROC Curve Components

The ROC curve displays several key metrics ?

import matplotlib.pyplot as plt
from sklearn import datasets, metrics, model_selection, svm

# Generate dataset and train model
X, y = datasets.make_classification(random_state=42)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, random_state=42)

clf = svm.SVC(probability=True, random_state=42)
clf.fit(X_train, y_train)

# Get predictions and probabilities
y_pred = clf.predict(X_test)
y_pred_proba = clf.predict_proba(X_test)[:, 1]

# Calculate ROC curve points
fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_proba)
auc_score = metrics.roc_auc_score(y_test, y_pred_proba)

print(f"AUC Score: {auc_score:.3f}")
print(f"Number of thresholds: {len(thresholds)}")

AUC Score: 0.966
Number of thresholds: 26

Key Metrics

Output

The ROC curve plot shows the classifier's performance across different threshold values. A perfect classifier would have an AUC of 1.0, while a random classifier would have an AUC of 0.5.

Conclusion

ROC curves are essential for evaluating binary classifiers, with AUC scores providing a single metric for model comparison. Use plot_roc_curve() for quick visualization and roc_curve() for detailed analysis of threshold effects.