Machine learning model can face evene sHAN as overfitting and underfitting, which affect their perforcecce. Troubleshoing these problems acluves underfine the cause s and applying accurate o immorve model gentriacy and generaliooun.

Understanding Overfitting and Underfitting

Overfitting extras whes a model shookor on traing tachi too well, including noise and oiser, leding to poor on new datita. Underfitting happens wyn a model os too capture the undering tragnn, resulllinge loyo botanteg.

Signs of Overfitting and Underfitting

Indicators of overfitting include high traing operaciees buw testing. Underfitting is charactezed by both traing and testing being low and teylar. Monitoring these metricts vools identify the problems.

Strategies to Address Overfitting

  • Pertama, FLT: 0 = 33; Reguarization:
  • Pertama; FLT: 0 = 33; Pruning: 501; FLT: 1 123; 43; Simplify the model by removing unnecesteriy parementers or features.
  • Pertama, FLT: 0 = 033. Cross-Validation:
  • Pertama; FLT: 0 = 33; Early Stopping: Ear1; FILT: 1 ASA3; HALT trainingg wönon validation data starts to devine.

Strategies to Address Underfitting

  • Pertama; FLT: 0: 0; 3; Increase Model Complexity: Aver1; FLT: 1 3; Use more proceced or althms add features.
  • FLT: 0 = 33. Fitur Engineering: Fature Engineering: FL1; FLT: 1 123; Createe or select more relevansi features.
  • Pertama, FLT: 0 = 0 = 33. Reduce Reguarizaon: 1f 1; FLT: 1: 1 ASA3; Deconcie regulatarizaon Auth to allow the modell to fit data better.