The 5 Classification Evaluation metrics every Data Scientist must know

What do we want to optimize for? Most of the businesses fail to answer this simple question.

Every business problem is a little different, and it should be optimized differently.

We all have created classification models. A lot of time we try to increase evaluate our models on accuracy. But do we really want accuracy as a metric of our model performance?

What if we are predicting the number of asteroids that will hit the earth.

Just say zero all the time. And you will be 99% accurate. My model can be reasonably accurate, but not at all valuable. What should we do in such cases?

Designing a Data Science project is much more important than the modeling itself.

This post is about various evaluation metrics and how and when to use them.