Abstract: Apparatus and associated methods relate to developing a predictive analytic model based on data records partitioned as a function of at least one relationship between parts and folds, assigning more than one part to test each fold, and assigning at least one part to test more than one fold; and evaluating the predictive analytic model based on more than one prediction determined for each observation in each test data record as a function of a predictive analytic model not trained on the test data record. In an illustrative example, the relationship between parts and folds may exclude some of the training data in common, with the same degree of overlap in the data between each pair of folds. Various examples may advantageously produce models built on each pair of folds having nearly equal pairwise-correlation of their predictions with models built on any other pair of folds.

Abstract: The present invention provides a method and system for automatically identifying and selecting preferred classification and regression trees. The invention is used to identify a specific decision tree or group of trees that are consistent across train and test samples in node-specific details that are often important to decision makers. Specifically, for a tree to be identified as preferred by this system, the train and test samples must both agree on key measures for every terminal node of the tree. In addition to this node-by-node criterion, an additional tree selection method may be imposed. Accordingly, the train and test samples rank order the nodes on a relevant measure in the same way. Both consistency criteria may be applied in a fuzzy manner in which agreement must be close but need not be exact.

Abstract: Methods and systems for automatically identifying and selecting preferred classification and regression trees are disclosed. Embodiments of the disclosed invention may be used to identify a specific decision tree or group of preferred trees that are predictively consistent across train and test samples evaluated against at least one node-specific constraint imposed by the decision-maker, while also having high predictive performance accuracy. Specifically, for a tree to be identified as preferred by embodiments of the disclosed invention, the train and test samples when evaluated node-by-node must agree on at least one key measure of predictive consistency. In addition to this node-by-node criterion, the decision-maker may adjust selection constraints to permit selection of a tree having a small number of node-by-node consistency disagreements, but with high overall tree predictive performance accuracy.

Abstract: Methods and systems for automatically identifying and selecting preferred classification and regression trees are disclosed. Embodiments of the disclosed invention may be used to identify a specific decision tree or group of preferred trees that are predictively consistent across train and test samples evaluated against at least one node-specific constraint imposed by the decision-maker, while also having high predictive performance accuracy. Specifically, for a tree to be identified as preferred by embodiments of the disclosed invention, the train and test samples when evaluated node-by-node must agree on at least one key measure of predictive consistency. In addition to this node-by-node criterion, the decision-maker may adjust selection constraints to permit selection of a tree having a small number of node-by-node consistency disagreements, but with high overall tree predictive performance accuracy.

Abstract: Methods and systems for automatically identifying and selecting preferred size classification and regression trees are disclosed. The invention is used to identify a specific decision tree or group of preferred size trees that are consistent across train and test samples in node-specific details that are often important to decision makers. Specifically, for a tree to be identified as preferred by this system, the train and test samples must both agree on key measures for every terminal node of the tree. In addition to this node-by-node criterion, an additional tree selection method may be imposed. Accordingly, the train and test samples rank order the nodes on a relevant measure in the same way. Both consistency criteria may be applied in a fuzzy manner in which agreement must be close but need not be exact.

Abstract: Methods and systems for automatically identifying and selecting preferred size classification and regression trees are disclosed. The invention is used to identify a specific decision tree or group of preferred size trees that are consistent across train and test samples in node-specific details that are often important to decision makers. Specifically, for a tree to be identified as preferred by this system, the train and test samples must both agree on key measures for every terminal node of the tree. In addition to this node-by-node criterion, an additional tree selection method may be imposed. Accordingly, the train and test samples rank order the nodes on a relevant measure in the same way. Both consistency criteria may be applied in a fuzzy manner in which agreement must be close but need not be exact.

Abstract: The present invention provides a method and system for automatically identifying and selecting preferred classification and regression trees. The invention is used to identify a specific decision tree or group of trees that are consistent across train and test samples in node-specific details that are often important to decision makers. Specifically, for a tree to be identified as preferred by this system, the train and test samples must both agree on key measures for every terminal node of the tree. In addition to this node-by-node criterion, an additional tree selection method may be imposed. Accordingly, the train and test samples rank order the nodes on a relevant measure in the same way. Both consistency criteria may be applied in a fuzzy manner in which agreement must be close but need not be exact.