Understanding and Managing Class Imbalance in Machine Learning Algorithms

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Class imbalance is a common issue in machine learning where the distribution of classes in the training data is uneven. In binary classification, it refers to a situation where one class significantly outnumbers the other. This imbalance can impact the performance of machine learning models, as they may become biased towards the majority class, leading to suboptimal predictive performance for the minority class. Here’s a comprehensive overview of class imbalance in machine learning:

1. Understanding Class Imbalance:

a. Definition:

• Class imbalance refers to a scenario where the distribution of instances among different classes in a dataset is significantly skewed.
• It occurs when the number of instances in one class is notably lower or higher than the others.

b. Example:

• Creadit Card Fraud Detection: The majority of credit card transactions are legitimate, while fraudulent transactions are relatively rare. An imbalanced dataset can result in a model that is biased towards classifying transactions as legitimate, potentially leading to overlooking fraudulent activities.
• Healthcare — Disease Diagnosis: Certain diseases, such as rare…