Uplift Model Graph

The graph represents the response on the vertical axis. The horizontal axis corresponds to observations, arranged by nodes. For each node, a black horizontal line shows the mean response. Within each split, there is a subsplit for treatment shown by a red or blue line. These lines indicate the mean responses for each of the two treatment groups within the split. The value ordering of the treatment column determines the placement order of these lines. As nodes are split, the graph updates to show the splits beneath the horizontal axis. Vertical lines divide the splits.

Beneath the graph are the control buttons: Split, Prune, and Go. The Go button only appears if there is a validation set. Also shown is the name of the Treatment column and its two levels, called Treatment1 and Treatment2. If more than two levels are specified for the Treatment column, all but the first level are treated as a single level and combined into Treatment2.

To the right of the Treatment column information is a report showing summary values relating to prediction. (Keep in mind that prediction is not the objective in uplift modeling.) The report updates as splitting occurs. If a validation set is used, values are shown for both the training and the validation sets.

RSquare

The RSquare for the regression model associated with the tree. Note that the regression model includes interactions with the treatment column. An RSquare closer to 1 indicates a better fit to the data than does an RSquare closer to 0.

Note: A low RSquare value suggests that there may be variables not in the model that account for the unexplained variation. However, if your data are subject to a large amount of inherent variation, even a useful uplift model may have a low RSquare value.

RMSE

The root mean square error (RMSE) for the regression model associated with the tree. RMSE is only given for continuous responses. For more details, see the Fitting Linear Models book.

The number of observations.

Number of Splits

The number of times splitting has occurred.

AICc

The Corrected Akaike Information Criterion (AICc), computed using the associated regression model. AICc is only given for continuous responses. For more details, see Likelihood, AICc, and BIC in the Fitting Linear Models book.

Uplift Decision Tree

The decision tree shows the splits used to model uplift. See Figure 6.4 for an example using the Hair Care Product.jmp sample data table. Each node contains the following information:

Treatment

The name of the treatment column is shown, with its two levels.

Rate

Only appears for two-level categorical responses. For each treatment level, the proportion of subjects in this node who responded.

Mean

Only appears for continuous responses. For each treatment level, the mean response for subjects in this node.

Count

The number of subjects in this node in the specified treatment level.

t Ratio

The t ratio for the test for a difference in response across the levels of Treatment for subjects in this node. If the response is categorical, it is treated as continuous (values 0 and 1) for this test.

Trt Diff

The difference in response means across the levels of Treatment. This is the uplift, assuming that:

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The first level in the treatment column’s value ordering represents the treatment.

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The response is defined so that larger values reflect greater impact.

LogWorth

The value of the logworth for the subsequent split based on the given node.

Figure 6.4 Nodes for First Split