How to solve class imbalance problem

1. How do you solve class imbalance problems in NLP?
2. How do you solve a class imbalance in Python?
3. How do you handle class imbalance in image classification?

How do you solve class imbalance problems in NLP?

The simplest way to fix imbalanced dataset is simply balancing them by oversampling instances of the minority class or undersampling instances of the majority class. Using advanced techniques like SMOTE(Synthetic Minority Over-sampling Technique) will help you create new synthetic instances from minority class.

How do you solve a class imbalance in Python?

Over-sampling data related to minority classes: Oversampling is a technique used to solve the class imbalance problem in machine learning models. It involves randomly selecting samples from the minority class and replicating them until the classes are balanced.

How do you handle class imbalance in image classification?

One of the basic approaches to deal with the imbalanced datasets is to do data augmentation and re-sampling. There are two types of re-sampling such as under-sampling when we removing the data from the majority class and over-sampling when we adding repetitive data to the minority class.