Standard Machine Learning Datasets for Imbalanced Classification

An imbalanced classification problem is a problem that involves predicting a class label where the distribution of class labels in the training dataset is skewed. Many real-world classification problems have an imbalanced class distribution, therefore it is important for machine learning practitioners to get familiar with working with these types of problems. In this tutorial, you will discover a suite of standard machine learning datasets for imbalanced classification. Standard Machine Learning Datasets for Imbalanced Classification Photo by Graeme Churchard, some rights reserved. Binary classification predictive modeling problems are those with two classes.