Uplift Tree Evaluator

The evaluation of uplift models differs much from other predictive analytics approaches. This node evaluates the quality of an Uplift Model by analyzing the real uplift in given bins compared to the predicted uplift. The best way to do so, is to convert and append the predicted uplift into a binned column. In most cases an Uplift Tree will only have a certain amount of possible outcomes (e.g. 25 different prediction values). These 25 values are considered as 25 bins. The Uplift Tree Evaluator now calculates the real uplift within the bins and compares it to the predicted uplift of the model. Similar uplift values will result in a good performance of the uplift model.

Options

Control group column

Column containing a flag to identify members of the control group.

Target group column

Column containing a flag to identify if costumers responded to a treatment or not.

Binned real uplift column

Column with bins, to calculate the real uplift in.

Predicted uplift column

Binned real uplift to calculate the real uplift per bin.

Control group column flag

Flag values used to mark members of the control group.

Target goup column flag

Flag values used to mark customers that have responded to a treatment.

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Workflows

• 2019_01_14_DYMATRIX Uplift ModelingKNIME Hub

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