What is imbalanced data in machine learning?

Imbalanced data in machine learning refers to datasets where the number of instances belonging to each class is significantly different. This can lead to issues during training and evaluation of machine learning models, as the algorithm may become biased towards the majority class and perform poorly on the minority class. Imbalanced data can also affect the accuracy and reliability of the model, as it may struggle to make accurate predictions for the underrepresented class. Techniques such as resampling, generating synthetic data, and cost-sensitive learning can be used to address imbalanced data in machine learning.

This mind map was published on 30 June 2024 and has been viewed 18 times.