Understanding Sensitive Imbalance and the F1 Score in Machine Learning

Introduction

In the realm of machine learning and data science, the performance of models is crucial. One of the common challenges faced during model evaluation is dealing with sensitive imbalance in datasets. This occurs when the classes in a classification problem are not represented equally, making it difficult for the model to perform well across all classes. To quantify and address this issue, various metrics like the F1 score are employed. In this article, we delve into what sensitive imbalance is, how it affects model performance, and why the F1 score is a critical measure for assessing models under such conditions.

Sensitive Imbalance: What Is It?

Sensitive imbalance refers to a scenario in which one or more classes in a dataset are significantly underrepresented compared to others. For example, in a medical dataset used to predict the presence of a rare disease, the majority of samples will likely be labeled as healthy (negative class), while a very small portion will be labeled as diseased (positive class). This imbalance can lead to a situation where the model becomes biased towards the majority class, often predicting the majority class more frequently because it has more examples to learn from.