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Addressing ML’s Reproducibility Crisis

Towards Data Science

You’re probably aware that machine learning (ML) has a reproducibility problem. Hundreds of pre-prints and papers are published every week in the ML space but too many can’t be replicated or…

A Common Misconception About Model Reproducibility

Daily Dose of Data Science

Today I want to discuss something extremely important about ML model reproducibility. Imagine you trained an ML model, say a neural network. It gave a training accuracy of 95% and a test accuracy of 9...

Reproducibility in Data Science

Towards Data Science

Science as a pursuit has always had Reproducibility at its core. After all, if a claim is made about the physical world, and the evidence does not support such a claim, it doesn’t matter how much…

Reproducible Machine Learning

Towards Data Science

The NeurIPS (Neural Information Processing Systems) 2019 conference marked the third year of their annual reproducibility challenge and the first time with a reproducibility chair in their program…

On Reproducibility

Towards Data Science

Reproducibility is important to science. A scientific result isn’t considered confirmed until multiple studies have reached the same conclusion. Also, new work is more efficient if it can build on…

How Reproducibility Crisis is Eating Away the Credibility of Machine Learning Technology?

Analytics Vidhya

Reproducibility of results has become a big challenge for Machine Learning models. We explore the challenges and possible solutions.

Achieve Reproducibility in your Machine Learning Project with these Two Tools

Towards Data Science

In machine learning, since there is no such thing as the best model, you may need to train different models and try out different parameters until you find the model that gives the highest accuracy…

Reproducible Machine Learning Results By Default

Machine Learning Mastery

Last Updated on August 16, 2020 It is good practice to have reproducible outcomes in software projects. It might even be standard practice by now, I hope it is. You can take any developer off the stre...

Introducing the Machine Learning Reproducibility Scale

Towards Data Science

Reproducibility in machine learning is a recurring topic, brought up both in research and industry. Lots of opinions, but no easy way to quantify or provide a standard for it. The Reproducibility Scal...

On the value of reproducibility

Towards Data Science

We arrived at a point in the history of science in which we have formally defined the so-called reproducibility crisis, known as the ongoing tendency to fail to reproduce or replicate results of…

Machine Learning code reproducibility using versioning

Towards Data Science

Machine Learning systems are based on 3 main components: data, code and model. The three components are interrelated in many ways and using a dependency control system, we can ultimately achieve…

Reproducibility

PyTorch documentation

Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms. Furthermore, results may not be reproducible between CPU and GPU executions, eve...