Nullifying the Bias from Imbalance Classes

Have you ever thought when could 95+% accuracy mean absolutely nothing? Welcome to the world of imbalanced classes.

Where one class vastly outnumbers the other. We would have to deal with this scenario in most of the sectors like finance, ad servings, healthcare and, vehicle failure, and much more. whether this transaction is fraudulent or not? where a person clicks on the ads or not, does the person corresponding to this x-ray has cancer or not, could be a breakdown in the middle of transit. Though there are only small probabilities of these above events it would be devastating if it happens, so as we would like to predict when they could happen and try to minimize our problems.

As machine learning engineers or data scientists, we have to help predict fraudulent transactions. Many techniques can be used to battle imbalance in a dataset they are

To understand to what extent these above methods help us I would like to first follow the normal method to show how badly it does. so that we could quantify the use of each method later.

Vanilla classification without SMOTE

There are various steps to be followed in a machine learning project:

Dataset loading and preparation

So you need a classification dataset that suffers from a class imbalance problem. Something like credit card fraud detection should do

Here’s how to load it with Python:

There are 31 columns which include time, amount, and target variable along with 28 other normalized columns. First, let’s explore the target class distribution:

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