Training tree-based machine-learning modeling algorithms for predicting outputs and generating explanatory data

A method comprising:

• generating a tree-based machine-learning model that is a memory structure comprising interconnected parent nodes and terminal nodes, wherein each parent node includes a respective splitting variable that causes the parent node to be connected via links to a respective pair of child nodes, wherein the terminal nodes includes respective representative response values based on values of the splitting variables, wherein generating the tree-based machine-learning model comprises:
• determining a splitting rule for partitioning data samples in a decision tree;
• partitioning, based on the splitting rule, data samples into a first tree region and a second tree region;
• computing a first representative response value from the data samples in first tree region and a second representative response value from the data samples in second tree region, wherein a monotonicity constraint for the decision tree is violated by a set of representative response values including the first and second representative response values, a representative response value for a closest lower neighboring region of the first tree region, and a representative response value for a closest upper neighboring region of the second tree region;
• applying a modification to the decision tree;
• computing, with the decision tree having the modification, a first modified representative response value from data samples partitioned into the first tree region and a second modified representative response value from data samples partitioned into the second tree region, wherein:
• the monotonicity constraint is satisfied by a modified set of representative response values, and
• the modified set of representative response values includes the first and second modified representative response values, the representative response value for the closest lower neighboring region of the first tree region, and the representative response value for the closest upper neighboring region of the second tree region;
• and
• outputting the decision tree having the modification.

A method comprising:

• receiving a target dataset having values for predictors, respectively;
• generating a risk assessment from the target data by, at least, applying a tree-based machine-learning model to the target dataset;
• and
• generating explanatory data identifying respective contributions of the predictors to the risk assessment, wherein the tree-based machine-learning model complies with a monotonicity constraint that facilitates the generation of the explanatory data.

A non-transitory computer-readable medium having program code stored thereon, wherein the program code is executable by processing hardware to cause the processing hardware to perform operations comprising:

• generating a tree-based machine-learning model that is a memory structure comprising interconnected parent nodes and terminal nodes, wherein each parent node includes a respective splitting variable that causes the parent node to be connected via links to a respective pair of child nodes, wherein the terminal nodes includes respective representative response values based on values of the splitting variables, wherein generating the tree-based machine-learning model comprises:
• determining a splitting rule for partitioning data samples in a decision tree;
• partitioning, based on the splitting rule, data samples into a first tree region and a second tree region;
• computing a first representative response value from the data samples in first tree region and a second representative response value from the data samples in second tree region, wherein a monotonicity constraint for the decision tree is violated by a set of representative response values including the first and second representative response values, a representative response value for a closest lower neighboring region of the first tree region, and a representative response value for a closest upper neighboring region of the second tree region;
• applying a modification to the decision tree;
• computing, with the decision tree having the modification, a first modified representative response value from data samples partitioned into the first tree region and a second modified representative response value from data samples partitioned into the second tree region, wherein:
• the monotonicity constraint is satisfied by a modified set of representative response values, and
• the modified set of representative response values includes the first and second modified representative response values, the representative response value for the closest lower neighboring region of the first tree region, and the representative response value for the closest upper neighboring region of the second tree region;
• and
• outputting the decision tree having the modification.