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Python 3
Classification Report

Classification Report

Classification Report
Generate the Classification Report
Explanation of the Metrics

Classification Report

The Classification Report provides a detailed summary of the model's performance by calculating several evaluation metrics for each class.

It includes:

Precision
Recall
F1-Score
Support

These metrics provide more insight than accuracy alone.

Generate the Classification Report

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred))

Explanation of the Metrics

Precision

Precision measures how many reviews predicted as fake were actually fake.

A high precision means the model produces fewer false alarms.

Recall

Recall measures how many actual fake reviews the model successfully identified.

A high recall indicates that the model misses fewer fake reviews.

F1-Score

The F1-Score combines Precision and Recall into a single metric.

It is particularly useful when both false positives and false negatives are important.

Support

Support indicates the number of actual samples belonging to each class in the testing dataset.

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Classification Report

Contents

Classification Report

Generate the Classification Report

Explanation of the Metrics

Precision

Recall

F1-Score

Support

Fake Review Detection System using Machine Learning

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