Automated rule generation with limited tree traversal

A non-transitory computer-readable storage medium storing program instructions executable on a computer system to perform operations comprising:

• generating a first rule of a rule set, wherein the generating includes:
• selecting a particular feature of a dataset of labeled training data having a plurality of features;
• training, based on the particular feature but not on other features of the plurality of features, a decision stump of depth 1;
• selecting a branch of the decision stump as an initial rule condition;
• training, using remaining features of the plurality of features, a decision tree that descends from the selected branch of the decision stump and that uses remaining ones of the plurality of features;
• extracting a branch of the decision tree having a greatest information gain, the branch of the decision tree specifying one or more remaining rule conditions;
• and
• combining the initial rule condition and the one or more remaining rule conditions to generate the first rule;
• and
• repeating the generating for additional rules of the rule set, wherein the initial rule condition of a given additional rule is generated using a different feature of the dataset.

A method, comprising:

• determining a first rule of a rule set by training a decision tree model that includes a root node at a first level of a decision tree and a plurality of nodes at lower levels of the decision tree, wherein training of the decision tree model includes:
• generating, based on a particular feature of a training dataset, the root node with at least one branch descending from the root node;
• selecting a branch of the root node as a selected root-level branch, with any remaining branches constituting unselected root-level branches;
• generating a remainder of the decision tree model by including nodes descending from the selected root-level branch, and without including nodes descending from the unselected root-level branches;
• setting, based on a path from the root node to one of the plurality of nodes having a greatest information gain, the first rule;
• and
• repeating the determining to determine additional rules of the rule set, wherein a given additional rule is generated using a different feature of the training dataset.