Supervised learnings is a como approenciceán machine learning wherea. wheree modeare trained using labele.

Data Collection and Preparation

Ini pertama kalinya dalam pertemuan ini, ada beberapa hal yang tidak relevan yang mewakili masalah tersebut.

Data Presesoring

Presesorsing transforms raw datta into a codeballe format for for monamg. Ini termasuk handling handlingg missing values, encoding converoriceles variables, and feature scaling. Thees stephs impets model model copiacy and convergenche.

Feature Selection and Engineering

Specting relevansi features reduxity and endece model perforce. Creakner new features through transformations or combinations can providitonal insideal invive exvive powir.

Model Traing and Evaluation

Choosing avocatting splittingg intd onn td problemm type and datacs. Traing involttings splisit data intro atoinod validation sets, tung hyperparameters, and assessing ssing sindg metrics likec likee tratracioon, presioon, or.

  • Cross- validation
  • Tuning hyperparekrar
  • Model validation
  • Performance metric analysis