Why is imbalanced data a problem

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.

1. What is the disadvantage of imbalanced data?
2. Why class imbalance is a problem?
3. What is the problem with imbalanced datasets in classification problems?
4. How would class imbalance affect your model?

What is the disadvantage of imbalanced data?

Disadvantages: It can discard useful information about the data itself which could be necessary for building rule-based classifiers such as Random Forests. The sample chosen by random undersampling may be a biased sample. And it will not be an accurate representation of the population in that case.

Why class imbalance is a problem?

Many practical classification problems are imbalanced. 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 datasets in classification problems?

It means that the model fails to identify the minority class yet the accuracy score of the model will be 95%. Thus our traditional approach of classification and model accuracy calculation is not useful in the case of the imbalanced dataset.

How would class imbalance affect your model?

When a class imbalance exists within the training data, machine learning models will typically over-classify the larger class(es) due to their increased prior probability. As a result, the instances belonging to the smaller class(es) are typically misclassified more often than those belonging to the larger class(es).