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Random Forest Model
The Random Forest model was used to analyze the EDA data generated from analyzing edge density, Green channel, Red Channel, Blue channel, edge density, Maximum and minimum blob size.
Classification Report
Accuracy: The model correctly predicted 89% of the test data. This is a measure of all correct predictions over all predictions made.
Metrics for Class 0 (Negative Class)
- Precision: 0.88 - When it predicts class 0, it is correct 88% of the time.
- Recall: 0.90 - Correctly identified 90% of all actual class 0 instances.
- F1-score: 0.89 - Indicates a good balance between precision and recall.
Metrics for Class 1 (Positive Class)
- Precision: 0.90 - When it predicts class 1, it is correct 90% of the time.
- Recall: 0.88 - Correctly identified 88% of actual class 1 instances.
- F1-score: 0.89 - Balanced, similar to class 0.
Additional Metrics
- Support: 750 occurrences for each class, indicating a balanced test set.
- Macro Average: 0.89 - Unweighted mean for precision, recall, and F1-score, indicating overall balanced performance.
- Weighted Average: Equal to the macro average due to balanced class support.
In summary, the model shows balanced performance with similar precision, recall, and F1-scores for both classes, performing slightly better in identifying class 0 instances than class 1 instances when considering precision and recall separately.