# Linear Regression example from textbook by Kotu and Deshpande 2015

Hi,

I'm new to RapidMiner. I am trying to follow the Linear Regression steps from p172-179 Ch5 in the textbook "Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner" (2015). The textbook is quite old and this is possibly an outdated way of performing this action?

I'm new to RapidMiner. I am trying to follow the Linear Regression steps from p172-179 Ch5 in the textbook "Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner" (2015). The textbook is quite old and this is possibly an outdated way of performing this action?

Unfortunately I am too new to the community to post an image of the process but if you google "Figure 5.10. Setting up a process to do the comparison between the unseen data and the model predicted values", the screenshot is at the bottom right of the science direct page returned.

The step I cannot get to work is the last Generate Attributes step. An attribute is created to calculate the difference between the predicted value and the actual value. The histogram of this difference is then viewed to check the distribution.

The textbook says to enter the formula: (predictedMEDV-MEDV)

Both of these are listed as "special attributes" when I view the function expressions input list

Both of these are listed as "special attributes" when I view the function expressions input list

The error I receive when running the process is: "The attribute MEDV is unknown"

When I view the example set, the field MEDV does not display.

But when I view the input port on the Generate Attributes operator, I can see the MEDV field there with a "prediction" label.

Any tips?

thanks

ETA: I have ended up generating an ID for the original data set and joining back to this at the end, so I can compare the original attribute with the predicted attribute

Any tips?

thanks

ETA: I have ended up generating an ID for the original data set and joining back to this at the end, so I can compare the original attribute with the predicted attribute

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## Answers

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