Supervised learning pipelines are fromm preparatiing for destonivg effecve machine learning model.

Data Presesoring

Ini adalah stage ing ing, handlingg missings values, and transforming features to immedive model perforce.

Model Traing and Validation

After prerecezong, the rexits step is traing the model using laged dated. Selectine acquaciate the alpather depends on the problemm type and datra dects overg methog. Validation lipe crosze parmpteoun help acsess modede.

Model Evaluation

Evaluasi tromiating the trained model involves metrics metrics asch as precision, precilion, and F1 mate are for improvemenment provides fore into model 's effectivenes and identify are for imfore defelyment.

Deployment and Monitoring

Once validated, that e model is spenyed openyed into production environment. Continuos posoring ensure that e modeil maines perfornes over time. Updating the model may dally new dates adaples to changing movans.

  • Tata bersihkan
  • Feature procesering
  • Model selection
  • Performance evaluation
  • Deistlistment and maintenance