Regression Methods
Abstract
This chapter covers two of the most popular function-fitting algorithms. The first is the well-known linear regression method used commonly for numeric prediction. We describe briefly the basics of regression and explain with the classic Boston Housing data set how to implement linear regression in RapidMiner. We also include a discussion on feature selection and provide some checkpoints for correctly implementing linear regression. The second is the more recent logistic regression method used for classification. We explain the basic concepts behind calculation of the logit and how this is used to transform a discrete label variable into a continuous function so that function-fitting methods may be applied. ...
Get Predictive Analytics and Data Mining now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
