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What is Data Balancing in EDA Techniques about?

The document discusses the importance of data balancing in exploratory data analysis, particularly in classification problems with imbalanced datasets. It outlines the negative impacts of imbalanced datasets, such as biased model learning and misleading performance metrics, and presents various techniques to address these issues, including under-sampling, over-sampling, and K-fold cross-validation. The document emphasizes the need for careful handling of imbalanced data to ensure effective model performance

Author

piyushkarnale68

Language

EN