Entropy, Free Full-Text

Differential replication is a method to adapt existing machine learning solutions to the demands of highly regulated environments by reusing knowledge from one generation to the next. Copying is a technique that allows differential replication by projecting a given classifier onto a new hypothesis space, in circumstances where access to both the original solution and its training data is limited. The resulting model replicates the original decision behavior while displaying new features and characteristics. In this paper, we apply this approach to a use case in the context of credit scoring. We use a private residential mortgage default dataset. We show that differential replication through copying can be exploited to adapt a given solution to the changing demands of a constrained environment such as that of the financial market. In particular, we show how copying can be used to replicate the decision behavior not only of a model, but also of a full pipeline. As a result, we can ensure the decomposability of the attributes used to provide explanations for credit scoring models and reduce the time-to-market delivery of these solutions.

Understanding the Role of Entropy in High Entropy Oxides

High-entropy-stabilized chalcogenides with high thermoelectric performance

How to teach entropy at post-16, CPD

Nihilism, Low Entropy

Entropy: How Decision Trees Make Decisions, by Sam T

Stream Soular Order - Entropy (Full Album Mix) by SOULAR ORDER

How to teach entropy at post-16, CPD

Entropy - Energy Education

Entropy Dropping Extract by Jan Bardi