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Handling Imbalance in Machine Learning by Amir Freer is a document available to read on EtoBox.
What is Handling Imbalance in Machine Learning about?
The document outlines strategies for addressing common issues in machine learning, specifically focusing on handling class imbalance, outliers, missing values, and categorical data. It discusses various techniques for managing class imbalance, including random undersampling, random oversampling, SMOTE, and Tomek Links. The presentation emphasizes the importance of understanding model performance through metrics and confusion matrices in the context of imbalanced datasets.
- Author
- Amir Freer
- Language
- EN