Overfitting and underfitting commo comoing comoing eons ion machine learning model. They afect the model 's ability to generalize froman datta to unseen data. Understanting their mathticil foundation is in an deviginr betterir modes apporades.

Persatuan Matematika

Overfitting excases a model learns noy to the e underlying pattern but t also the noise noise triiningg data. Mathematically, it results is a low trainingr arr but hierror on dates. Underfitting happens whee moigo to reacano.

Model Bias-variance ini menjelaskan fenomena ini. Vigh bias supo (Bias Bias), while high variance mopes tend to overfit. Balancingbias and varianpe essential for optimal model scucpe.

Indikators Mathematikal

Metrics sHAN as mean ssared error (MSE) and cross- validation scores help iffpy overfitting and underfitting. A thoft gap between training and validation errors inclutes overfitting. Converseby errory on both dagettes suging.

Teknik Solutions and

Severala methodus address overfitting and underfitting. Retarization techques likee L1 and L2 add penalties to model complexity. Cross- validation hells in tuningg hyperparameters. Simplifying model reduces overfitting, while sinImprixlates.

  • Regularization
  • Cross- validation
  • Selektioun Feature
  • Model complexity adjument
  • Pao Autmentation