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

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

Imbalanced data can lead to biased models that perform poorly on the minority class, affecting accuracy.

Can you give me an example of how this affects predictions?

Sure! In a fraud detection model, if 95% of transactions are legitimate, the model may ignore fraudulent cases.

What techniques can be used to handle imbalanced data?

Common techniques include resampling methods, using different algorithms, and applying cost-sensitive learning.

How can I evaluate a model trained on imbalanced data?

You can use metrics like precision, recall, F1-score, and the ROC-AUC curve to evaluate performance.