# Copying Machine Learning Classifiers

## TL;DR

This paper presents a comprehensive framework for copying machine learning classifiers without prior knowledge of their parameters or training data, enabling enhanced interpretability, fairness, and feature addition.

## Contribution

It introduces a novel theory and practical framework for model-agnostic copying of classifiers, including metrics and guidelines for synthetic data generation.

## Key findings

- Copies can improve interpretability and fairness.
- The framework is validated on well-known datasets.
- Copies can add new features and enhance existing models.

## Abstract

We study model-agnostic copies of machine learning classifiers. We develop the theory behind the problem of copying, highlighting its differences with that of learning, and propose a framework to copy the functionality of any classifier using no prior knowledge of its parameters or training data distribution. We identify the different sources of loss and provide guidelines on how best to generate synthetic sets for the copying process. We further introduce a set of metrics to evaluate copies in practice. We validate our framework through extensive experiments using data from a series of well-known problems. We demonstrate the value of copies in use cases where desiderata such as interpretability, fairness or productivization constrains need to be addressed. Results show that copies can be exploited to enhance existing solutions and improve them adding new features and characteristics.