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
|
Parameter |
Description |
|---|---|
| Category | Model Validation |
| Data source type | HD |
| Send 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 | Notes or helpful information about this operator's parameter settings. When you enter content in the Notes field, a yellow asterisk appears on the operator. |
| Dependent Column | Define the column to use as the class variable. |
Output
