18. A non-transitory storage medium storing instructions executable by an electronic data processing device to perform a method including (i) learning a set of multi-task decision trees (MT-DT's) for a set of tasks including at least two tasks using different subsets of a training set wherein the learning of each MT-DT includes learning decision rules for nodes of the MT-DT that maximize an aggregate information gain (IG) that aggregates single-task IG values for tasks of the set of tasks and (ii) constructing a multi-task (MT) predictor as a weighted combination of outputs of the learned set of MT-DT's.
18. A non-transitory storage medium storing instructions executable by an electronic data processing device to perform a method including (i) learning a set of multi-task decision trees (MT-DT's) for a set of tasks including at least two tasks using different subsets of a training set wherein the learning of each MT-DT includes learning decision rules for nodes of the MT-DT that maximize an aggregate information gain (IG) that aggregates single-task IG values for tasks of the set of tasks and (ii) constructing a multi-task (MT) predictor as a weighted combination of outputs of the learned set of MT-DT's. 23. The non-transitory storage medium of claim 18 wherein the constructing includes: combining the outputs of the MT-DT's using a multi-task adaptive boosting (MT-AdaBoost) algorithm.