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.
The node supports custom CSS styling. You can simply put CSS rules into a single string and set it as a flow variable 'customCSS' in the node configuration dialog. You will find the list of available classes and their description on our documentation page.
Displays a lift chart visualization.
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