Lift (HD)

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 HD
Sends output to other operators No
Data processing tool MapReduce

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.
  • 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.

Output

Visual Output
Lift diagram. Used for model comparison if more than one model is supplied by the preceding operators.



Data Output
None. This is a terminal operator.
Related reference