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Author's profile photo Andreas Forster

Custom R Component – Logistic Regression

This component adds a logistic regression to SAP Predictive Analysis.

A confusion matrix is automatically shown when training or testing the model. When applying the model on data, for which the actual classificationis not known, a frequency plot of the predicted classification is displayed.



Please note that this component is provided as-is without any guarantee or support.


The classifier column must contain only the values 0 and 1.

The column names must not include a minus sign.

R libraries lattice, ggplot2 and caret have to be installed.


These parameters can be set by the user.

Parameter Description
Predictor Columns Names of the predictor columns.
Classifier Column Name of the target column. Must contain only the values 0 and 1.
Configuration Optional R configuration for the logistic regression.
Classification Threshold

Threshold for classifying either 0 or 1. Default is 0.5.

Output Columns added by this Component

Column Description
PredictedProbability Predicted probability. Value between 0 and 1.
PredictedValue Predicted value based on the classification threshold and the predicted probability.

How to Implement

The component can be downloaded as .spar file from GitHub. Then deploy it as described here. You just need to import it through the option “Import/Model Component”, which you will find by clicking on the plus-sign at the bottom of the list of the available algorithms.


If you want to try this logistic regression on some sample data, you can use the Adult dataset as used in the article on the Naive Bayes Algorithm. Just remember that the column names must not include a minus sign and ensure to transform the target column into binary 0 and 1 coding.


Configure the component appropriately. To get started you only have to set the predictor and classifier columns. For the remaining settings you can keep the default values.


Run the model and you can see the predicted values either a raw data or in the embedded confusion matrix.



Now save the trained model. Then add it as additional component into the testing-branch of the analytical flow.


Execute the component and go in the “Results” panel to the “Custom Chart” and you will see that another confusion matrix has been created. The component was able to identify automatically that the real classification is already known. If the classifier column (that was specified when training the model) exists in the dataset, the component assumes that it is tested on already classified data. Therefore it displays the confusion matrix to help evaluate the model’s performance.


When applying the model on new data, for which the real classification is not known, the component will display a frequency plot of the predictions.


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