Lift (DB)

Visualizes the performance of a classification model. It applies in general to classification models (for example, CART, Decision Tree, Logistic Regression, Naive Bayes, Neural Network, and Alpine Forest).

Information at a Glance

Category	Model Validation
Data source type	DB
Sends output to other operators	No
Data processing tool	n/a

While a cumulative gains chart shows the total number of events captured by a model over a given number of samples, a lift curve shows the ratio of a model to a random guess. Lift charts show how a model performs compared to random guessing given x number of samples.

For example, suppose a population has an average response rate of 1%, but a certain model has identified a segment with a response rate of 10%. That segment has a "lift" of 10.0 (10%/1%).

By ranking quantiles of data by lift, you can see which areas have the most lift and thus which the model performs best on. For more information and examples about lift, see here.

Input

A data set from the preceding operator.
Optional - One or more model(s) from the preceding operator(s). The models must be a classification model. The following models are supported.
- CART
- Decision Tree
- Logistic Regression
- Naive Bayes
- Neural Network
- Alpine Forest

Configuration

Parameter	Description
Notes	Any notes or helpful information about this operator's parameter settings. When you enter content in the Notes field, a yellow asterisk is displayed on the operator.
Dependent Column	Define the column to use as the class variable.
Value to Predict	The value that represents the event to analyze. Note: The value of this column must match the data as it is stored in the database that matches how it appears in the data explorer. If the user defines a Boolean Dependent Column with 1s and 0s, the user must use 1 or 0 as the Value to Predict. If the column uses True and False, the user must use "True" or "False" as the Value to Predict.
Use Model	Specify whether the evaluation uses a model from preceding operator(s) or the data in the prediction columns of the input data set. If true, at least one model operator must directly precede it. If false, the prediction columns must be present in the input data set from its preceding operator. Default value: true.
Confidence Columns	Choose the list of columns in the input data set to compare to the dependent column.