Predictor screening is useful for identifying significant predictors from a large number of candidates. Suppose you had hundreds of Xs and needed to determine which of those were most significant as predictors of an outcome.
The predictor screening platform uses a bootstrap forest to identify potential predictors of your response. For each response, a bootstrap forest model using 100 decision trees is built. The column contributions to the bootstrap forest model for each predictor are ranked. Because the bootstrap forest method involves a random component, column contributions can differ when you rerun the report. For more information about decision trees, see Partition Models.