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A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers
Differential replication through copying refers to the process of replicating the decision behavior of a machine learning model using another model that possesses enhanced features and attributes.
Differential Replication in Machine Learning
When deployed in the wild, machine learning models are usually confronted with data and requirements that constantly vary, either because of changes in the generating distribution or because external constraints change the environment where the model operates.
Sampling Unknown Decision Functions to Build Classifier Copies
Copies have been proposed as a viable alternative to endow machine learning models with properties and features that adapt them to changing needs.
Copying Machine Learning Classifiers
We study model-agnostic copies of machine learning classifiers.
Towards Global Explanations for Credit Risk Scoring
We use a private residential mortgage default dataset as a use case to illustrate the feasibility of this approach to ensure the decomposability of attributes during pre-processing.
