Subject archive for "model-management," page 13

In this recorded webcast, Daniel Emaasit introduces model-based machine learning and related concepts, practices and tools such as Bayes' Theorem, probabilistic programming, and RStan.

At last week's useR! R User conference, I spoke on digital provenance, the importance of reproducible research, and how Domino has solved many of the challenges faced by data scientists when attempting this best practice. More on the topic, and a recording of the talk, below.

We’re excited to announce a set of big new features that make it easier for you to find and reuse past data science work in your team and organization. We’re calling these features “Discoverability,” and they encompass powerful ways to search, tag, and get recommendations about relevant analytical work.

An interesting topic we often hear data science organizations talk about is “unit testing.” It’s a longstanding best practice for building software, but it’s not quite clear what it really means for quantitative research work — let alone how to implement such a practice. This post describes our view on this topic, and how we’ve designed Domino to facilitate what we see as relevant best practices.

In this post, I discuss a method for A/B testing using Beta-Binomial Hierarchical models to correct for a common pitfall when testing multiple hypotheses. I will compare it to the classical method of using Bernoulli models for p-value, and cover other advantages hierarchical models have over the classical model.

Domino provides a great way to iterate on analytical models by letting you run many experiments in parallel on powerful hardware and automatically track their results. Recently we added two powerful new features to make that workflow even easier: Run Comparisons and Diagnostic Statistics.