1. A method implemented on a general purpose computer with processor-executable program instructions configured to direct at least one processor and at least one stored data table, the at least one stored data table comprising a plurality of records and a plurality of columns and including data useful for creating and evaluating a predictive model, to automatically select a preferred tree or sub-tree among a plurality of trees, the method comprising: accessing the at least one stored data table; partitioning the data useful for creating and evaluating a predictive model to identify a training set of records and a test set of records within the data table records; constructing, by predictive analysis of the training set of records, a collection of at least one fitted tree or sub-tree, each tree or sub-tree having at least one node, at least one node of each tree or sub-tree being a terminal node; determining at least one node decision criterion useful to examine a distribution in data of at least one attribute; determining a distribution in data of at least one attribute for each node of each tree or sub-tree, the data including at least one of: train data, test data, validation data, or historical data; determining at least one lift ratio threshold; choosing at least one predictive consistency check method from a plurality of predictive consistency check methods, the plurality of predictive consistency check methods comprising: direction match agreement, and rank match agreement; determining at least one predictive consistency check failure tolerance threshold for each chosen predictive consistency check method; determining at least one prediction for each node of each tree or sub-tree as a function of at least one node decision criterion and a distribution in data of at least one attribute, the data including at least one of: train data, test data, validation data, or historical data; calculating test set lift ratio and training set lift ratio for each node of each tree or sub-tree as a function of: at least one prediction made in each node, and at least one node decision criterion; determining at least one agreement statistic corresponding to each chosen predictive consistency check method and associated with each node of each tree or sub-tree; determining at least one predictive accuracy performance measure associated with each tree or sub-tree; calculating the at least one agreement statistic corresponding to each chosen predictive consistency check method and associated with each node of each tree or sub-tree for two or more of: training set records, test set records, validation set records, or historical records; calculating a predictive accuracy performance score for each tree or sub-tree as a function of the at least one predictive accuracy performance measure and based on records limited to the test set of records; evaluating the at least one predictive consistency check method for each node of each tree or sub-tree, for training set records and test set records, in accordance with at least one of: an exact match or a fuzzy match, the at least one predictive consistency check method evaluated as a function of: at least one lift ratio, at least one lift ratio threshold, and at least one agreement statistic; identifying a first set of trees or sub-trees satisfying each chosen predictive consistency check method evaluated for each node of each tree or sub-tree; identifying a second set of trees or sub-trees not satisfying each chosen predictive consistency check method evaluated for each node of each tree or sub-tree; selecting a third set of trees or sub-trees from the second set of trees or sub-trees, the third set of trees having predictive consistency check failures within the chosen predictive consistency check failure tolerance threshold for each chosen predictive consistency check method; selecting as the preferred tree or sub-tree that tree or sub-tree from the first and third sets of trees or sub-trees having the best predictive accuracy performance score; and providing access to a decision maker to the preferred tree or sub-tree for generating predictive analytic output as a function of input data, wherein selection of the preferred tree or sub-tree is based on: one or more of direction agreement or rank match agreement determined for each node of each tree or sub-tree as a function of both the train set of records and the test set of records, predictive accuracy performance score determined for each tree or sub-tree as a function of records limited to the test set of records, and predictive consistency check method failures within a predictive consistency check failure tolerance threshold.