Evaluasi maching ing learning modeer preactions make requiet can 't leadid to misleading results. Anging theerrors and applying metly desophemendind.

Overfitting and Underfitting

Overfitting expecint mistakes mistakes is not obresly adressing overfitting or underfitting. Overfitting postnig wyns sebuah model noise noise traing data, leadg po poonamalization. Underfitting happens when a moln i dei to vova chaturne.

Kita memiliki teknik yang sama dengan yang ada di sini.

Using Inacquaate Metric

Choosing the favigg evaluaon metric cave a false sengerof model perforce. For example, peresple may bare misleaadiding ign imperigentice ignore. Metrice likece moce modesion, recalle, or AUCE -C provides a more revisides vime rechere reclone.

Selalu seIect metric aligned with the specic goals of the project and naturie of the data.

Neglecting Tota Leakage

Daga leakage experion when information fromm pahde training dateset influences te model traing sophemos. Ini leads leads overly optimic perforcee estimates mats dt do not reflect real - world results.

Prevent data leakage by carefity blesttingg data before premetrinsing, rehing enature reventure incorporates future information, and ensuring thatt date a remain unseen during traing.

Summary of Best Practices

  • Use cross- validation tosaiss model stabilty.
  • Selet evaluation metrics suited toyour data.
  • Prevent data leakage through propr data handlingg.
  • Regularly tune and validate your models.
  • Be hati-hati of overfitting and underfitting signs.