Class imbalance is a common conclue in deep learning where some classes have e relevantly fewer examples than other s. This imbalance can lead to biased models that perforum poorly on minority classes. Detersing this issue endives techniques such as data appening and cost- sensitive learning.

Data Sampling Techniques

Data samping settings thee distribution of training data to balance class represention. Common methods include oversambing minority classes and undersamping majority classes. These techniques help thee model learn equally from all classes.

Costsensitive Learning

Cost- sensitive learning assigns higher misclassification costs to minority classes. This approach accessages thee model to pay more attention to underrepresented classes during traing, improvisin overall performance on imbalanced datasets.

Implementation Strategies

Effective strategies include combining data sampling with cost- sensitive loss funktions. Additionally, techniques like focal loss can focus training on hard-to-classify examples, further metigating class imbalance issues.

  • Oversampling minority classes
  • Podvzorek majority classes
  • Aplikační class váhy in loss funkce
  • Using focal loss