1. An apparatus comprising: a multi-task machine learning component for learning a set of T tasks, where T is greater than or equal to three, based on a set of examples wherein the examples are represented by features of a set of features, the multi-task machine learning component comprising a digital processing device configured to bootstrap aggregate features of the set of features by sampling the set of features M times uniformly and with replacement to generate M possibly overlapping sub-sets of the set of features defining M feature bags where M is an integer equal to or greater than two and learn an ensemble of M base rules wherein each base rule is learned for a different one of the M feature bags and comprises a multi-task decision tree (MT-DT) having nodes comprising decision rules for tasks of the set of tasks, wherein the multi-task machine learning component learns each base rule by: (i) learning a decision rule for a task of the set of T tasks wherein the learned decision rule defines a node of the MT-DT and the possible outputs of the decision rule define links to child nodes for which the learned node is the parent node, and (ii) recursively repeating the learning (i) for each child node with the set of T tasks trimmed to remove the task to which the decision rule of the parent node is directed, wherein the base rule is an MT-DT with T levels corresponding to the T tasks comprising the set of nodes generated by the learning operations (i) and (ii); and an inference component comprising a digital processing device configured to predict a result for at least one task of the set of tasks for an input item represented by features of the set of features using the learned ensemble of base rules.