Lift Chart (legacy)

Creates a lift chart. Additionally, a chart for the cumulative percent of responses captured is shown. A lift chart is used to evaluate a predictive model. The higher the lift (the difference between the "lift" line and the base line), the better performs the predictive model. The lift is the ratio between the results obtained with and without the predictive model. It is calculated as number of positive hits (e .g. responses) divided by the average number of positives without model. The data table must have a column containing probabilities and a nominal column, containing the actual labels. At first, the data is sorted by probability, divided into deciles, then the actual labels are counted and the average rate is calculated.


Column containing true labels
Nominal column containing the actual labels, e. g. if a person responded
Response Label
The label for a positive value (hit).
Column containing score (probabilities)
Numeric column containing the predicted score in probabilities of the model
Interval width in %
The width in which the data is separated before counting.

Input Ports

Data table

Output Ports

Data table sorted by probability


Lift Chart
The lift chart




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