From the frontmatter: We recommend this book! Deep Learning for Coders with fastai and PyTorch uses advanced frameworks to move quickly through concrete, real-world artificial intelligence or automation tasks. This leaves time to cover usually neglected topics, like safely taking models to production and a much-needed chapter on data ethics. […]
I would like to re-share links to our free vtreat data preparation system introduction videos, which show you what sort of machine learning problems vtreat can help you with. Python vtreat introduction video (PyData LA 2019), slides here. R vtreat introduction video (Why R? Foundation). The idea is: instead of […]
Nina Zumel has updated our training page to describe the Python data science intensive for software engineers we have been conducting for a couple of years. This is private group training in addition to our usual R training for scientists, and consulting offerings. Please check it out.
Data science is often a case of brining the tools to the problems and data, instead of insisting on bringing the problems and data to the tools. To support cross-language data science we have been working on cross-language tools, documentation, and training.
Win Vector LLC’s Dr. Nina Zumel has had great success applying y-aware methods to machine learning problems, and working out the detailed cross-validation methods needed to make y-aware procedures safe. I thought I would try our hand at y-aware neural net or deep learning methods here.
I would like to re-share vtreat (R version, Python version) a data preparation documentation for machine learning tasks. vtreat is a system for preparing messy real world data for predictive modeling tasks (classification, regression, and so on). In particular it is very good at re-coding high-cardinality string-valued (or categorical) variables […]
Students have asked me if it is better to use the same cross-validation plan in each step of an analysis or to use different ones. Our answer is: unless you are coordinating the many plans in some way (such as 2-way independence or some sort of combinatorial design) it is […]
A big thank you to Dmytro Perepolkin for sharing a “Keep Calm and Use vtreat” poster! Also, we have translated the Python vtreat steps from our recent “Cross-Methods are a Leak/Variance Trade-Off” article into R vtreat steps here. This R-port demonstrates the new to R fit/prepare notation! We want vtreat […]
We have a new Win Vector data science article to share: Cross-Methods are a Leak/Variance Trade-Off John Mount (Win Vector LLC), Nina Zumel (Win Vector LLC) March 10, 2020 We work some exciting examples of when cross-methods (cross validation, and also cross-frames) work, and when they do not work. Abstract […]
Here is a quick, simple, and important tip for doing machine learning, data science, or statistics in Python: don’t use the default cross validation settings. The default can default to a deterministic, and even ordered split, which is not in general what one wants or expects from a statistical point […]