Supervised learninge model are uused to make predications based on laged dated. Ini articIe beanche concept and underfitting os essentiag for creetine effecnive movee.

Overfitting IV Supervised Models

Overfitting excases whln a model learns the trainingg datta too well, including noise noise and outliers. As a resalt entry, it performs evile on w, unseen data. Overfitted model to engkau complex and have variance.

Underfitting IV Supervised Models

Underfitting happens a model is too o yore capture te underlying patterns ite whee data. Ini results is n poor perfornce on both traing and test datres. Underfitted models have high bias and low varianpe.

Strategies to Prevent Overfitting

  • Pertama, FLT: 0 = 33; Retariarization:
  • Pertama, FLT: 0: 0 = = Torde3; Cross-Validation:
  • Pertama; FLT: 0 = 33; Pruning: 501; FLT: 1 123; 433; Simplifying models sHAN as deusion trees reduky unnecessies.
  • Pertama; FLT: 0 = 33; Early Stopping: Ear1; FILT: 1 123; OLT3; Halting trainingg before model overfits the data.

Strategies to Prevent Underfitting

  • Pertama; FLT: 0: 0 = 33. Increadising Complexity Model: lega1; FLT: 1 3; Using more features or complex alpithms.
  • FLT: 0 = Fature Engineering: Feature Engineering: FE1; FLT: 1 FLT: 1 FLT; Creaking new features to bettir direpresentasikan data pola.
  • Reducing Regularizaon: lear1; FLT: 1; LT; Minimizing penalties tidak membatasi conferbility model.