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Python 3
ROC Curve

ROC Curve

Import Required Functions
Calculate Prediction Probabilities
Compute the ROC Curve
Plot the ROC Curve
Explanation

ROC Curve

The Receiver Operating Characteristic (ROC) Curve evaluates the model's ability to distinguish between fake and genuine reviews across different classification thresholds.

A curve closer to the top-left corner indicates better model performance.

Import Required Functions

from sklearn.metrics import roc_curve

Calculate Prediction Probabilities

y_prob = model.predict_proba(X_test)[:,1]

Compute the ROC Curve

fpr, tpr, thresholds = roc_curve(y_test, y_prob)

Plot the ROC Curve

plt.figure(figsize=(7,6))

plt.plot(fpr, tpr, label='Logistic Regression')

plt.plot([0,1],[0,1],'--')

plt.xlabel("False Positive Rate")

plt.ylabel("True Positive Rate")

plt.title("ROC Curve")

plt.legend()

plt.show()

Explanation

The ROC Curve illustrates the trade-off between the True Positive Rate and the False Positive Rate.

A curve farther away from the diagonal reference line indicates better classification performance.

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ROC Curve

Contents

ROC Curve

Import Required Functions

Calculate Prediction Probabilities

Compute the ROC Curve

Plot the ROC Curve

Explanation

Fake Review Detection System using Machine Learning

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Spring Boot Tutorial

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Apache Cordova Tutorial

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