Los functions are essentiala interial ion watsee learning as they measpee difference between predictus outputs and actuadel labels. They guire the trainininin g by providing a metric optimic te. dignore loss ard ureliding to pome opplicane.

Mean Squared Error (MSE)

Ini adalah satu-satunya cara untuk menentukan apa yang terjadi.

FLT: 0 = MS3; MS3 = (1 / n) Y = 1 / 1; FLT: 1: 1; ASA3; i 1; ASA1; FLT: 2: 33; - F11; FL322323T; 2323232T; 32232T; 32232T; 123223T; 1T; 123222222222323T; 22222222222222223T;

Di mana Anda 113; FLT 0; 33; & lt; i & gt; FLT: 1; 13; Is te true value, Averone; FLT: 2; 23; 41; i fLLT: FLT: 3: 3 td td td predicated, and iimonamoramoramitus.

Cross- Entroppy Lops

Cross--entropy loss is primarily ustior clasfication tasks. Ini adalah imperior the dissimilary between yang memprediksikan probabiliten distributioy ane true distribution. For binary clacification, the formula is:

11; Syari1; FLT: 0 = 33; Cross-Entroppy = - Ylog (Aver1) log (1 - y)% 1 - 1- 1- 1f; 171; FLT: 1 MIL333;

Dimana i y is the true labell (0 or 1), and dolpies the predicated of the positive class. Ini adalah loss function penalizes incorression predications more whee modei is confident but fagg.

Sampeison and Usale

MSE is codeablle for continuoue output variables, while crosspe entroppy is idel for catelitordil data. Choosing the asulateate loss function to th probleme type and the naturie of the output.

  • Masalah Regression
  • Masalah klasik fication
  • Resesionaris konversi Model
  • Handling of outliers