25 Techniques to Handle And Monitor Imbalanced Datasets in Machine Learning with Python

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Handling imbalanced datasets is a crucial aspect of machine learning, as it can significantly impact the performance of models, particularly when the classes of interest are unevenly distributed. Here’s a deep dive into various techniques for addressing imbalanced datasets in machine learning using Python:

1. Understanding Imbalanced Datasets:

a. Imbalance Ratio:

• The imbalance ratio is a quantitative measure that represents the ratio of the number of samples in the minority class to the majority class in an imbalanced dataset.
• Imbalance Ratio = (Number of Samples in Minority Class) / (Number of Samples in Majority Class)
• A higher imbalance ratio indicates a more severe imbalance in the dataset, with the minority class being significantly underrepresented compared to the majority class.

b. Impact on Models:

• Models may have limited exposure to the minority class during training, leading to challenges in accurately predicting instances from that class.
• Imbalanced datasets can cause machine learning models to be biased towards the majority class.