Class balancing

1. What is class balancing?
2. What is class imbalance example?
3. Why do we use class balancing in machine learning?
4. What is class imbalance and how do you deal with it?

What is class balancing?

What is Class Balancing? In machine learning, class balancing means balancing classes with unbalanced samples.

What is class imbalance example?

Class imbalance is normal and expected in typical ML applications. For example: in credit card fraud detection, most transactions are legitimate, and only a small fraction are fraudulent. in spam detection, it's the other way around: most Emails sent around the globe today are spam.

Why do we use class balancing in machine learning?

Balancing a dataset makes training a model easier because it helps prevent the model from becoming biassed towards one class. In other words, the model will no longer favour the majority class just because it contains more data.

What is class imbalance and how do you deal with it?

One of the widely adopted class imbalance techniques for dealing with highly unbalanced datasets is called resampling. It consists of removing samples from the majority class (under-sampling) and/or adding more examples from the minority class (over-sampling).