Methods and systems for training a decision-tree based machine learning algorithm (mla)

Abstract

Methods and servers for of training a decision-tree based Machine Learning Algorithm (MLA) are disclosed. During a given training iteration, the method includes generating prediction values using current generated trees, generating estimated gradient values by applying a loss function, generating a first plurality of noisy estimated gradient values based on the estimated gradient values, generating a plurality of noisy candidate trees using the first plurality of noisy estimated gradient values, applying a selection metric to select a target tree amongst the plurality of noisy candidate trees, generating a second plurality of noisy estimated gradient values based on the plurality of estimated gradient values, generating an iteration-specific tree based on the target tree and the second plurality of noisy estimated gradient values, and storing, the iteration-specific tree to be used in combination with the current generated trees.