Launch the Uplift Platform

Launch the Uplift platform by selecting Analyze > Consumer Research > Uplift.

Figure 6.4 Uplift Platform Launch Window 

For more information about the options in the Select Columns red triangle menu, see “Column Filter Menu” in Using JMP.

Y, Response

Assigns one or more columns to be analyzed.

X, Factor

Assigns one or more columns to be used as factors.

Treatment

Assigns a categorical treatment column. If the treatment column contains more than two levels, the first level is treated as one treatment level and the remaining levels are combined into a second treatment level.

Weight

Assigns a numeric column that contains a weight for each observation in the data table. A row is included in the analysis only when its weight is greater than zero.

Freq

Assigns a frequency variable to this role. This is useful for summarized data.

Validation

Assigns a numeric column that defines the validation sets. This column should contain at most three distinct values:

– If there are two values, the smaller value defines the training set and the larger value defines the validation set.

– If there are three values, these values define the training, validation, and test sets in order of increasing size.

– If the validation column has more than three levels, the rows that contain the smallest three values define the validation sets. All other rows are excluded from the analysis.

The Uplift platform uses the validation column to train and tune the model or to train, tune, and evaluate the model. For more information about validation, see “Validation in JMP Modeling” in Predictive and Specialized Modeling.

If you click the Validation button with no columns selected in the Select Columns list, you can add a validation column to your data table. See “Make Validation Column” in Predictive and Specialized Modeling.

By

Produces a separate report for each level of the By variable. If more than one By variable is assigned, a separate report is produced for each possible combination of the levels of the By variable.

Validation Portion

The portion of the data to be used as a validation set. Enter a value between 0 and 1.

Informative Missing

If selected, enables missing value categorization for categorical predictors and informative treatment of missing values for continuous predictors.

Ordinal Restricts Order

If selected, restricts consideration of splits to those that preserve the ordering.