Linear Regression in RapidMiner

Regression models are useful and understandable models which are used for prediction and data fitting. RapidMiner provides simple tool for regression which I am going to illustrate how to use it in this post.

The first step is to import the training data by using appropriate Read operator. Then you may change the attribute type of your target field (dependent variable) to label by using Set Role operator. Adding the Linear Regression operator, your model should look like the figure below.

The min tolerance property of Linear Regression operator is confidence level or alpha level in statistic language. Now, you can import your test data and use the Apply Model operator to predict the data. Your model should look like the following figure.

If you connect the weight port of the Linear Regression operator to the result port of process windows, you can see the weights of independent variables in the separate table. I ran my model which contains 5 independent variables and one dependent variable. the following graphs show the results. RapidMiner provides you with the statistics result related to the regression model and the equation and adds a prediction field to the test data set. You can export the results to the excel by using write to Excel operator