What is a Classification Report?

• A Classification Report is one of the performance evaluation metrics of a classification-based machine learning model.

• It displays your model's precision, recall, F1 score and support.

• It provides a better understanding of the overall performance of our trained model.

• To understand the classification report of a machine learning model, one should know all the metrics displayed in the report.

• Below are all the metrics shown by a classification report:

1. Precision:

   • Precision is defined as the ratio of true positives to the sum of true and false positives.

2. Recall:

   • Recall is defined as the ratio of true positives to the sum of true positives and false negatives.

3. F1 Score:

   • The F1 score is the weighted harmonic mean of precision and recall.
   • The closer the value of the F1 Score is to 1.0, the better the expected performance of the model.

4. Support:

   • Support is the number of actual occurrences of the class in the dataset.
   • It doesn't vary between models, it just diagnoses the performance evaluation process.