After spot checking algorithms on your problem and tuning the better few, you ultimately need to select one or two best models with which to proceed. This problem is called model selection and can be vexing because you need to make a choice given incomplete information. This is where the test harness you create and […]
Midwest.io is was a conference in Kansas City on July 14-15 2014. At the conference, Josh Wills gave a talk on what it takes to build production machine learning infrastructure in a talk titled “From the lab to the factory: Building a Production Machine Learning Infrastructure“. Josh Wills is a the Senior Director of Data […]
Jeremy Howard, formally of Kaggle gave a presentation at the University of San Francisco in mid 2013. In that presentation he touched on some of the broader benefits of machine learning competitions like those held on Kaggle. In this post you will discover 5 points I extracted from this talk that will motivate you to […]
The predictions you make with a predictive model do not matter, it is the use of those predictions that matters. Jeremy Howard was the President and Chief Scientist of Kaggle, the competitive machine learning platform. In 2012 he presented at the O’reilly Strata conference on what he called the Drivetrain Approach for building “data products” […]
David Kofoed Wind posted an article to the Kaggle blog No Free Hunch titled “Learning from the best“. In the post, David summarized 6 key areas related to participating and doing well in competitive machine learning with quotes from top performing kagglers. In this post you will discover the key heuristics for doing well in […]
What if you could use a predictive model to find new combinations of attributes that do not exist in the data but could be valuable. In Chapter 10 of Applied Predictive Modeling, Kuhn and Johnson provide a case study that does just this. It’s a fascinating and creative example of how to use a predictive […]
In their book Applied Predictive Modeling, Kuhn and Johnson comment early on the trade-off of model prediction accuracy versus model interpretation. For a given problem, it is critical to have a clear idea of the which is a priority, accuracy or explainability so that this trade-off can be made explicitly rather than implicitly. In this […]
Project layout is critical for machine learning projects just as it is for software development projects. I think of it like language. A project layout organizes thoughts and gives you context for ideas just like knowing the names for things gives you the basis for thinking. In this post I want to highlight some considerations […]
For as long as I have been participating in data mining and machine learning competitions, I have thought about automating my participation. Maybe it shows that I want to solve the problem of building the tool more than I want to solve the problem at hand. When working on a dataset, I typically spend a […]
BigML is a fresh new and interesting machine learning as a service company based out of Corvallis, Oregon, USA. In a previous post, we reviewed the BigML service, the key features and the ways in which you could use this service in your business, on you side project or to present to clients. In this […]
