Mean Square Error (MSE) and Mean Absolute Error (MAE) – Regression

Regression is a technique to establish a relationship between independent and dependent variables. The relationship assumes the linearity between them. It is very useful technique to predict the future value of dependent variable. Simple example of dependent variable (y) and independent variable (x) is as under

y = w0 + w1.x ...(1)

Where w0 and w1 are weights.

The values of weights decide the values of y based on value of x. For different weights, value of y maybe different with same x. Let’s understand with example and we represent this y as ybar, refer Figure1.

Next question came to our mind is, how to decide the values of weights. Although there are multiple methods available, but best is to use optimization algorithm called Gradient Descent.

Gradient Descent tracks the error calculated between the y and ybar and update the weights until the error is in acceptable level or sufficient iterations are complete. There are two very interesting techniques to calculate errors are mean square error (MSE) and mean absolute error (MAE).

To be continues with MSE.....