Why accuracy is not a best measure ??

Classification accuracy alone is not considered a good measure. Why?

Let’s take covid-19  data, predicting  as Positive (1) and ‘Negative(0)’ is said to be 100% accuracy in Classification problem. But, practically 100% accuracy or 0% error is impossible.

rom Confusion Matrix we get more metrics to evaluate the model,

True positives: Target variable labeled as positive that are actually positive

False positives: Target variable labeled as positive that are actually negative

True negatives: Target variable labeled as negative that are actually negative

False negatives: Target variable labeled as negative that are actually positive

Recall: Recall in this context is defined as the number of true positives divided by the total number of elements that actually belong to the positive class (i.e. the sum of true positives and false negatives, which are items which were not labeled as belonging to the positive class but should have been).

Precision :The ratio of correct positive predictions to the total predicted positives. Recall — Also called Sensitivity, Probability of Detection, True Positive Rate. The ratio of correct positive predictions to the total positives examples.

From the above metric it is evident that it is risk to have high false negative, high precision and high accuracy together is a good measure of evaluating but practically both does not maintain there balance in model.

Here we have F1 Score, which is nothing but mean of Recall and Precision. But its not simple mean rather it is harmonic mean.

Conclusion:

Accuracy is not always best or always poor measure for assessing. In cases of imbalance data we are provided with many metrics like Recall, Precision, F1 Score and much more. Measure of assessing depends on business scenario.