In a regression exercise, we estimate the continuous variable value. In such a scenario, our predictions can be lower than the actual value or higher; that is, the error value could be either positive or negative. In such a scenario, the objective function translates to minimizing the sum of squared values of the difference between the actual and predicted values of each of the observations in the dataset.
In mathematical terms, the preceding is written as follows:
In the given equation:
- SSE stands for the sum of squared errors
- y refers to the actual value of the dependent variable
- y' refers to the estimated ...
