Regularization is a techque usuad in machine learning to prevent overfitting by adding a penalty y te loss function. Ini helps s generalize better to unseek data by defraginy overly completions.

Teory of Regularization

Reguarization recodeces addeonai to objective function durting traing. Theese terms penalize model pareters, proggging simpler modes tont are lesa likely ty po fit noize ite traing.

Teknik Common Regularization

  • L1 Regularizaon: 13.FLT: 0: 0 Adens absolute of coefisien cients to the loss function, proming sparsity.
  • L2 Regularizaon: 131; FLT: 0: 0: 3O; L2 Regularizaon: 1f; FLT: 1: 1; Add the squared of koefisien, mendorong bobot secitiek.
  • FLT: 0 = 33; Dropout: 1f; FLT: 1 1f 3; Abo3; Randomly dropts units during traing to reducé reliance on specic neuroon.
  • Pertama; FLT: 0 = 33; Early Stopping: Ear1; FILT: 1: 1 FLT; H3; Stops trainingg when perfornc o validation data start to devine.

Calculations and Implementation

Dalam resission, for exiple, L2 regulaarization modifies te cost function as s folloves:

Loss = Sum of ssared errors + leva * Sum of ssared babot

Dimana itu terjadi, itu adalah peraturan paragorrite controllingg the penalty stheth.

Best Practices

Wun implementing regulatarization, consider the following best practices:

  • Use cross- validation to tune regulaarization parametera.
  • Start with simple movie and gracially improvasty se complexity.
  • Combine multiple regulaarization techques if neeariery.
  • Monitor validation perforce to astrod underfitting or overfitting.