Feature scalinge is a cruciala step ion preparinge data for machine learning allithms. Ini tidak sengaja bahwa e range of feature values s to immediv model convergence speece speeds. Understanding the mathtice fomathreations focudations helps ips ivereconcectes.

Mathematikal Fountations of Feature Scaling

Feature scaling methods are baseza on mathematikal transformations tont modufy distribution of data. Common techquees includes normalization and standardization. Normafition rescales features tre tos specicic range, typically ascalry 10, 1, 13333ugt; ustarhd, ushig fora:

11; Syaria1; FLT: 0 Aver3; Normalzed value = (x - myn) / (max - myn) System 1; FLT: 1 13; Aver3;

Standardization transforms data to have a meat of 0 and a standard deviation of 1, using:

Ascend-value = (x-Aver1) / ASA1; FLT: 1: 1 Astang3; Aver3;

Teknik Praktek for Feature Scaling

Implementing feature scalinge involves opposing that are method batanah on thad and the alolither. For experiple, alpiththms likee k-neerest neighlas and neuworks bath fromm normalizatioom, while linear n revission and logissioten reptisdoarn.

Teknis Common include:

  • Min- Max Scaling
  • Z- Score Standardization
  • Romust Scaling
  • MaxAbs Scaling

Ini adalah imporant to fit scaling paremeters on te traing data and apply the transformation to the data to prevent data leakagag.