Class imbalance i a common concerge in deep learningg where some classes have concerantli fewer examples than thans thans. Tiss imbalance can lead to biased models that perform poorly on minority class. Címzett tis issue contrukes technokes such as data dating and costi- sensitive learningig.

Data Sampling Techniques

Data mintating adaps the distribution of training data to balante class represpation. Common metods include oversampling minority classes and d undersampling majority classes. These technolques help the model learn equally fromaly all classes.

Cost- sensitive Learning- color

Cost- sensentive learningg assigns higher misclass to minority class. Tiss approcach concentiages the model to pay more atteniol to underpressed classes during trainig, improving overall performance on imbalanced datasets.

Végrehajtási stratégia

Effective strategies include combining data sampling with cost-sensitive loss funkcions. Additionally, technokes like focel los cas focus trainin og on hard-to-class sample, further mitigating class imbalanche issues.

  • Oversampling minority classes
  • Mintavételen aluli majority classes
  • Applying class weights in loss funkcions
  • Usingfocol loss