Applying the Linear Regression operator on the Polynomial data set

1. RapidMiner Linear
Regression Tutorial Processes
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2. Applying the Linear Regression operator on
the Polynomial data set
• The 'Polynomial' data set is loaded using the Retrieve operator. The Filter Example Range operator is applied
on it.
• The first example parameter of the Filter Example Range parameter is set to 1 and the last example
parameter is set to 100.
• Thus the first 100 examples of the 'Polynomial' data set are selected.
• The Linear Regression operator is applied on it with default values of all parameters.
• The regression model generated by the Linear Regression operator is applied on the last 100 examples of the
'Polynomial' data set using the Apply Model operator.
• Labeled data from the Apply Model operator is provided to the Performance (Regression) operator. The
absolute error and the prediction average parameters are set to true.
• Thus the Performance Vector generated by the Performance (Regression) operator has information
regarding the absolute error and the prediction average in the labeled data set.
• The absolute error is calculated by adding the difference of all predicted values from the actual values of the
label attribute, and dividing this sum by the total number of predictions.
• The prediction average is calculated by adding all actual label values and dividing this sum by the total
number of examples. You can verify this from the results in the Results Workspace.
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4. Detailed steps:
1. Create a subfolder under Local Repository called
“LinearRegressionTutorial”.
2. Click on File, New Process, Blank Process.
3. Drag Polynomial from Samples, Data to the process design.
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6. 4. Run the model
5. Explore the results.
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8. Observation: There are a total of 200
examples.
6. Complete the following process design with the parameters set
for both Filter Example Range
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10. 7. Run the model. Save the process as
RegressionTutorial1.
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11. PerformanceVector
PerformanceVector:
root_mean_squared_error: 81.300 +/- 0.000
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