Mechanisms are provided for detecting interesting decision rules from a set of decision rules in a tree ensemble. Each tree in the tree ensemble is traversed in order to assign each individual data record from a set of data records to an identified leaf node in each tree. Predicted values are determined for the tree ensemble based on predictions provided by each leaf node to which each individual data record is assigned. Interesting sub-indices for decision rules from the set of decision rules are determined and, for each decision rule corresponding to the leaf nodes in the tree ensemble, the sub-indices are combined into interestingness index It. The decision rules are ranked corresponding to the leaf nodes in the tree ensemble according to the associated value of the interestingness index It and a subset of the decision rules corresponding to the leaf nodes in the tree ensemble are reported.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a data processing system, for detecting interesting decision rules from a set of decision rules in a tree ensemble, the method comprising: traversing each tree in the tree ensemble in order to assign each individual data record from a set of data records in an evaluation data set to an identified leaf node in a set of leaf nodes in each tree; determining predicted values defined by the tree ensemble based on predictions provided by each leaf node to which each individual data record is assigned; determining interesting sub-indices for decision rules from the set of decision rules corresponding to the leaf nodes in the tree ensemble; for each decision rule corresponding to the leaf nodes in the tree ensemble, combining the sub-indices into interestingness index It; ranking the decision rules corresponding to the leaf nodes in the tree ensemble according to the associated value of the interestingness index It; and reporting a subset of the decision rules corresponding to the leaf nodes in the tree ensemble in order to provide a notification of the interesting decision rules in the tree ensemble. 2. The method of claim 1, wherein the subset of the decision rules corresponding to the leaf nodes in the tree ensemble is at least one of a predetermined number of decision rules, a percentage of the total number of decision rules, or decision rules whose interestingness index It is above a predetermined threshold. 3. The method of claim 1, wherein each decision rule in the set of decision rules comprise at least one of variables, constraints, and the predicted target value on a path to the corresponding leaf node. 4. The method of claim 1, wherein determining the interesting sub-indices for decision rules corresponding to the leaf nodes in the tree ensemble are performed for categorical targets or continuous targets. 5. The method of claim 4, wherein determining the interesting sub-indices for decision rules corresponding to the leaf nodes in the tree ensemble for categorical targets comprises: identifying a proportion of data records P(R(t)) determined by the leaf node t where the decision rule is accurate, R(t) being the event that the decision rule based on the node t is accurate; identifying a proportion of data records P(E(t)) determined by the leaf node t where the tree ensemble model is accurate, E(t) being an event that the ensemble prediction is accurate based on a data record determined by node t; determining a proportion P(Ē(t)) that the ensemble prediction is inaccurate based on a data record determined by node t by defining P(Ē(t))=1−P(E(t)); identifying a proportion of data records P(E(t)R(t)) determined by the node t that are predicted accurately by both the ensemble model and the decision rule; identifying a proportion of data records P(Ē(t)R(t)) determined by the node t that are predicted inaccurately by both the ensemble model and the decision rule; determining a first sub-index of interestingness I1t based on prediction agreement between the ensemble model and the decision rule as I1t=P(E(t)R(t))+P(Ē(t)R(t)); determining a second sub-index of interestingness I2t on the decision rule accuracy as I2t=P(R(t)); and determining a third sub-index of interestingness I3t on the ensemble model accuracy as I3t=P(E(t)). 6. The method of claim 4, wherein determining the interesting sub-indices for decision rules corresponding to the leaf nodes in the tree ensemble for continuous targets comprises: determining a standardized measure value D(t); determining a first sub-index of interestingness I1t between a decision rule and the ensemble prediction based on the conformance requirement as I1t=1−D(t); identifying decision rules that predict either high or low values of the target variable to be the most interesting; determining an effect size measure corresponding to a t-test; transforming the effect size measure into an interestingness sub-index with values between 0 and 1; defining a second sub-index of interestingness I2t of the decision rule accuracy as: I 2 t = { 0 , p vlaue  ( t ) ≥ sig f  ( E S  ( t ) ) , p vlaue  ( t ) < sig wherein sig is a specified significance level, pvalue(t) is a p-value from the t-test, Es(t) is the effect size measure, and f(x) is a monotone cubic interpolation function; determining an R squared metric for the ensemble model on data records determined by the leaf node t, determining R squared as 1 minus the relative error, where the relative error is given by the sum of squared errors for the ensemble model divided by the total sum of squares; and defining a third sub-index of interestingness I3t for accuracy of the ensemble model prediction to be I3t=R2(t). 7. The method of claim 1, wherein the interestingness index It is a product of a first sub-index of interestingness I1t, a second sub-index of interestingness I2t, and a third sub-index of interestingness I3t. 8-21. (canceled)