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.
Options
Data Options
General Plot Options
Axis Configuration
View Controls
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Views
- Interactive View: Lift Chart
- Displays a lift chart visualization.
Workflows
01_AnalyticsKNIME Hub- 01_Evaluating_Classification_Model_PerformanceKNIME Hub
- 01_Guided_Analytics_for_ML_AutomationKNIME Hub
- 01_Guided_Analytics_for_ML_AutomationKNIME Hub
- 01_Guided_Analytics_for_ML_AutomationKNIME Hub
- Show all 59 workflows
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Developers
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