AI News, Testing Machine Learning Models on Dual Coding Principles

Testing Machine Learning Models on Dual Coding Principles

This post intends to propose a technique termed as Dual Coding for testing or performing quality control checks on machine learning models from quality assurance (QA) perspective.

The idea behind dual coding testing is to test the quality of the program by testing two different implementations of the same program (one being the main program) for a given set of inputs and comparing their outputs for correctness.

In this post, you will learn some of the following topics: Applying the dual coding theory to machine learning, it would mean testing the models created using two different algorithms while using the same or most common set of features.

The diagram below represents the testing technique based on principles of dual coding theory: In case, there are predictions from the main model which differs from those made from alternate models, one could raise the defects in the bug tracking system for further analysis by the data scientists.

Given that dual coding testing is about testing the output (prediction) of a model by comparing the outputs with that of outputs from other models created using different algorithms, this could be automated in the following manner: The diagram below represents the above automation: The following represents some of the skills required for conducting dual coding tests or writing automation tests: (adsbygoogle = window.adsbygoogle ||

The primary idea taken from the dual coding theory is that human brains maintain two different mental representations including verbal and non-verbal (visual) systems to learn (get trained) about a particular topic.

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