Improving Linear Regression by Reducing Residual Errors

Here, we discuss the basic underpinnings of "boosting" and "gradient boosting". Focus on improving the estimation of a target function by computing "residuals" or errors– differences between the predicted and actual. If you follow the concept, it will become easier to use this idea to improve accuracy in linear regression, for example, by using the gradient boosting options available in many libraries.