Evaluating Classification Models Performance

False Positives & False Negatives:

Below is the way Logistic Regression comes up with. Here intent is to explain False Positive and False Negatives which model can predict.

- False Positive -   Model predicted positive, but actually it is False
- False Negative - Model predicted Negative, but actually it is True (More Risky)

Confusion Matrix:

Confusion Matrix is used to evaluate performance of model to see correct/incorrect predictions made by regression/classification models. Below is how confusion matrix evaluate the performance of model.

Hope this helps!!

Arun Manglick