What is class imbalance evaluation in machine learning?

What is class imbalance evaluation in machine learning?

Class imbalance evaluation refers to techniques used to assess the performance of models when the classes are not equally represented.

Why is class imbalance a problem in machine learning?

Class imbalance can lead to biased models that perform well on majority classes but poorly on minority classes, affecting overall accuracy.

What metrics should I use to evaluate models with class imbalance?

You should consider metrics like precision, recall, F1-score, and the area under the ROC curve (AUC-ROC) for better insights.

How can I handle class imbalance in my dataset?

You can handle class imbalance by using techniques like resampling, synthetic data generation (like SMOTE), or adjusting class weights.