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.









