1. Accuracy --> Total correct prediction/ Total number of instance.
2. Recall --> Recall is calculated as the number of true positives / total positives and false negatives.
3. Precision --> The precision measures the model's accuracy in classifying a sample as positive.
4. F1 score --> The F1-score combines the precision and recall of a classifier into a single metric by taking their harmonic mean.