Overfitting excases wheg a machine learnino it model learns traing tata too well, including noise and outliers, which reduces its on new datta. Reguarization techques helfitting boty adding strasting to mow del, promorizenaIiotig.

Understanding Regularization

Reguarization memperkenalkan model additionai to loss function during traing.

Teknik Common Regularization

  • L1 Regularizaon (Lasso): FLT: 1: 1 AFL3; Add that absolute e of coeticients to the lostion, promoting sparsity.
  • L2 Regularizaon (Ridge): FLT: 1: 1 AFL3; Add the squared of coeticients, suggging migher bobot.
  • FLT: 0 = 33; Dropout: 1f; FLT: 1: 1 Atr3; Randomly menonaktifkan neuroing traing in neural to prevents co- adation.
  • Pertama; FLT: 0 = 33; Early Stopping: Ear1; FILT: 1: 1 FLT; H3; Stops trainingg when perfornc o validation data start to devine.

Real- World Examples

Ini adalah recogition tasks, applying droptout sophs neural networcs generalize better unseen images. For linear linear regssion moving housing prices, L2 regulaarizazion reduzazios overfitting brow shing largine, leag coefisien, leag returnigo.

Innatural language modeze, early stopping is used prevent overfitting during traing of lmpage, ensuring they perform well on text data. Theese tecqueare essentiahas iun domains to improve del robustness.