Squared Error Loss [Regression]

Description

Mean squared error (MSE) is the most commonly used loss function for regression. The loss is the mean overseen data of the squared differences between true and predicted values

• Use case: Regression problems
• When to use: Is typically used when you are solving regression problems where the goal is to predict continuous numerical values. Squaring ensures that both positive and negative differences contribute equally to the loss.
• Key Property: Sensitive to outliers. Large errors are penalized more due to squaring.

• Example applications:

  • Predicting house prices
  • Estimating sales revenue
  • Forecasting temperature

Formula

Normal:

Regularized:

Gradient Descent

Normal:

Regularized:

Sample Derivative Calculation for Linear Regression