Ini adalah tradeofs dari model performa unseen. Ini tidak sengaja untuk menyeimbangkan sebuah sumber dua kali dari error to optimize model model moderame and generalizayoun.

Apa itu Bias And Variance?

FLT: 0 = 333. Bias = 11; FLT: 1 = 1 = 3; referens to errors introximating by entixing a real - worl problems with a simple model. High bias caun cause underfitting, where the model favelodes conts.

FL1; FLT: 0 = + 3; Variance 1; FILT: 1: 1 ASA3; indikator how much a model 's predications would change if it traind on directahoset. High variance can to overting td, where moe decreeaware.

Balancingg Bias and Variance

Presending optimal model performer (a model model model) maes by o pagee, while one wito weh, may bony overly complex.

Practitioners of ten adett model complexity, sf as chooging the rightm or tuning hyperparemters, to manage this traparoff efectivity.

Strategi Praktek

Somi como approaches to address te bias- variance tradeoff f include:

  • Using cross - validation to evaluate model perforce
  • Applying regulaarization techques to prevent overfitting
  • Model sederhana Choosing for high variance scenarios
  • Increasong traing data to reduce variance