Removing Unequal Data Distribution Bias from Datasets for Binomial Classification

In the realm of machine learning, achieving accurate and reliable results often hinges on the quality of the dataset being used. One common challenge that arises in binary classification tasks is unequal data distribution bias. When one class significantly outnumbers the other, the model tends to favor the majority class, leading to biased and inaccurate predictions. In this article, we will explore techniques to address this issue and create a balanced dataset for binomial classification. Understanding the Problem: Before delving into solutions, it's crucial to grasp why unequal data distribution bias can be problematic. Consider a medical diagnosis scenario where only a small percentage of patients have a rare disease. If the dataset predominantly contains healthy individuals, the model may struggle to identify the disease accurately due to the lack of examples from the minority class. Resampling Techniques: 1. Oversampling: - Oversampling involves generating synthetic examples for the minority class to balance the dataset. - Popular oversampling methods include SMOTE (Synthetic Minority Over-sampling Technique) and ADASYN (Adaptive Synthetic Sampling). <a href="https://medium.com/@hssparks13/removing-unequal-data-distribution-bias-from-datasets-for-binomial-classification-14f7817aaad4">Read More</a>