Class imbalance problem in machine learning

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 is class imbalance in machine learning?
2. Why is class imbalance a problem machine learning?
3. What is the problem with imbalanced data in machine learning?
4. What is a class imbalance problem how can it be solved?

What is class imbalance in machine learning?

A classification data set with skewed class proportions is called imbalanced. Classes that make up a large proportion of the data set are called majority classes. Those that make up a smaller proportion are minority classes.

Why is class imbalance a problem machine learning?

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 the problem with imbalanced data in machine learning?

Imbalanced data is a common problem in machine learning, which brings challenges to feature correlation, class separation and evaluation, and results in poor model performance.

What is a class imbalance problem how can it be solved?

Definition. Data are said to suffer the Class Imbalance Problem when the class distributions are highly imbalanced. In this context, many classification learning algorithms have low predictive accuracy for the infrequent class. Cost-sensitive learning is a common approach to solve this problem.