Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines
This work addresses the need for interpretable machine learning, offering a method for local and global interpretation, though it appears incremental as an extension of existing neural additive models.
The authors tackled the problem of interpreting black-box models by proposing a method that uses an ensemble of gradient boosting machines to create generalized additive models, demonstrating its efficiency through numerical experiments on synthetic and real datasets.
A method for the local and global interpretation of a black-box model on the basis of the well-known generalized additive models is proposed. It can be viewed as an extension or a modification of the algorithm using the neural additive model. The method is based on using an ensemble of gradient boosting machines (GBMs) such that each GBM is learned on a single feature and produces a shape function of the feature. The ensemble is composed as a weighted sum of separate GBMs resulting a weighted sum of shape functions which form the generalized additive model. GBMs are built in parallel using randomized decision trees of depth 1, which provide a very simple architecture. Weights of GBMs as well as features are computed in each iteration of boosting by using the Lasso method and then updated by means of a specific smoothing procedure. In contrast to the neural additive model, the method provides weights of features in the explicit form, and it is simply trained. A lot of numerical experiments with an algorithm implementing the proposed method on synthetic and real datasets demonstrate its efficiency and properties for local and global interpretation.