Tackling Imbalanced Data Using imbalanced-learn, Part 1: Under-Sampling

In the field of machine learning, working with imbalanced datasets can present a significant challenge. Imbalanced data occurs when the distribution of classes in the dataset is uneven, with one class being dominant compared to the others. This can lead to biased models that perform poorly on the minority class. In this article, we will explore how to address imbalanced data using the Road Accidents UK dataset and the imbalanced-learn Python package.

This is the first article in a series …

artificial intelligence challenge data data science dataset datasets distribution imbalanced-data learn machine machine learning part sampling undersampling