Dealing With Imbalanced Classes in Machine Learning
summary
Covers what class imbalance is and practical ways to handle skewed datasets in classification problems using algorithms and techniques (such as resampling, algorithmic adjustments, or specialized methods implied by 'various algorithms and subtle techniques'). Also explains how to evaluate models under imbalance and highlights small but important practical considerations when applying these approaches.
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