“1. Think carefully about which projects you take on.2. Use as much data as you can from as many places as possible.3. Don’t just use internal customer data.4. Have a clear sampling strategy.5. Always use a holdout sample.6. Spend time on ‘throwaway’ modelling.7. Refresh your model regularly.8. Make sure your insights are meaningful to other people.9. Use your model in the real world.” Rachel Clinton ( January 7, 2015 )

Christie Aschwanden writes:

Bayesian deep learning methods often look like a theoretical curiosity, rather than a practically useful tool, and I’m personally a bit skeptical about the practical usefulness of some of the work. However, there are some situations a decent method of handling and representing residual uncertainties about model parameters might prove crucial. These applications include active learning, reinforcement learning and online/continual learning.

I love the formattable package, but I always struggle to remember its syntax. A quick Google search reveals that I’m not alone in this struggle. This post is intended as a reminder for myself of how the package works – and hopefully you’ll find it useful too!

Introduction In this blog post, I’ll use the data that I cleaned in a previousblog post, which you can downloadhere. If you want to follow along,download the monthly data.

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Many R users appear to be big fans of “code capturing” or “non standard evaluation” (NSE) interfaces. In this note we will discuss quoting and non-quoting interfaces in R.

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Scikit-learn is a free machine learning library for Python. It features various algorithms like support vector machine, random forests, and k-neighbours, and it also supports Python numerical and scientific libraries like NumPy and SciPy.