What is class imbalance problem

What is the Class Imbalance Problem? It is the problem in machine learning where the total number of a class of data (positive) is far less than the total number of another class of data (negative).

1. What does it mean by class imbalance problem?
2. What is class imbalance example?
3. What is class imbalance and how do you deal with it?
4. What is class imbalance problem and why it is a challenge for prediction?

What does it mean by class imbalance problem?

The class imbalance problem typically occurs when there are many more instances of some classes than others. In such cases, standard classifiers tend to be overwhelmed by the large classes and ignore the small ones.

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.

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).

What is class imbalance problem and why it is a challenge for prediction?

Imbalanced classification is specifically hard because of the severely skewed class distribution and the unequal misclassification costs. The difficulty of imbalanced classification is compounded by properties such as dataset size, label noise, and data distribution.