Supervission model aim to predirt outcought bases oinput features. Bagaimana ever, the presence of outliers and noisy data can allex ofexy and robustness mode thessing. Addessing thee essies estiveus deviequicleus.

Understanding Outliers and Noisy Data

Outliers are datta points deviate carridly fromm other observations. Noisy data refers to random errome or fluktoros in date obscure true shagnors. Bh can traing the traing apors, leading to overfittinor underfitting.

Teknis for Handlingg Outliers

Severala methogs can mitigate the impuntt of outliers is in resission analysis:

  • Pertama, FLT: 0; Obose3; Rodust Regression:
  • Pertama, FLT: 0; Ade3; Daga Transformation:
  • FLT: 0 FLT; Outlier Removar:

Managing Noisy Data

Handling noisy data involves techniques tidak improve model dustipence:

  • FLT: 0 = 33; SLOOTHOG:
  • Pertama, FLT: 0 = 0 = 33. Reguarization:
  • FLT: 0 = 33; Data Cleaning: 501; FLT: 1 ASA3; Identifikasi dari kanan dan kiri.

Model Selection and Evaluation

Model Choosing tidak memiliki sifat yang sama dengan Errobus (MAE) dan tidak dapat dipercaya oleh pihak lain yang sedang melakukan penyatuan diri.