Most Used Loss Functions To Optimize Machine Learning Algorithms

Most Used Loss Functions To Optimize Machine Learning Algorithms

This article gives us a brief overview of the most used loss functions to optimize machine learning algorithms. We all use machine learning algorithms to solve various complex problems and select them based on the loss function value and evaluation metrics.

This article gives us a brief overview of the most used loss functions to optimize machine learning algorithms. We all use machine learning algorithms to solve various complex problems and select them based on the loss function value and evaluation metrics. But do we know that we are selecting the correct loss function for our algorithm? If not, then let’s find out.

Mainly the loss functions are divided into three categories:

Regression loss functions

  1. Mean Squared Error
  2. Mean Squared Logarithmic Error
  3. Mean Absolute Error
  4. Binary classification loss functions

Binary Cross Entropy

  1. Hinge Loss
  2. Squared Hinge Loss

*Multi-class classification loss functions *

  1. Multi-class cross entropy
  2. Sparse multi-class cross entropy 
  3. Kullback Leibler divergence loss

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