What is the impact of imbalanced data on machine learning metrics?

What is the impact of imbalanced data on machine learning metrics?

Imbalanced data can skew metrics like accuracy, precision, and recall, leading to misleading evaluations.

Can you explain how it affects precision and recall specifically?

Sure! In imbalanced datasets, a model might predict the majority class well, inflating accuracy while precision and recall for the minority class drop.

What are some techniques to handle imbalanced data?

Common techniques include resampling methods like oversampling the minority class, undersampling the majority class, and using algorithms that are robust to imbalance.

How can I evaluate my model if my data is imbalanced?

You can use metrics like F1-score, ROC-AUC, and confusion matrices, which provide a better understanding of model performance on imbalanced data.