Last Updated on July 5, 2019 The Systematic Process For Working Through Predictive Modeling ProblemsThat Delivers Above Average Results Over time, working on applied machine learning problems you develop a pattern or process for quickly getting to good robust results. Once developed, you can use this process again and again on project after project. The more robust and developed your process, the faster you can get to reliable results. In this post, I want to share with you the skeleton […]

Last Updated on June 21, 2016 The test options you use when evaluating machine learning algorithms can mean the difference between over-learning, a mediocre result and a usable state-of-the-art result that you can confidently shout from the roof tops (you really do feel like doing that sometimes). In this post you will discover the standard test options you can use in your algorithm evaluation test harness and how to choose the right options next time. Randomness The root of the […]

Last Updated on August 21, 2016 When you first start out with machine learning you load a dataset and try models. You might think to yourself, why can’t I just build a model with all of the data and evaluate it on the same dataset? It seems reasonable. More data to train the model is better, right? Evaluating the model and reporting results on the same dataset will tell you how good the model is, right? Wrong. In this post […]

Last Updated on June 20, 2019 When you build a model for a classification problem you almost always want to look at the accuracy of that model as the number of correct predictions from all predictions made. This is the classification accuracy. In a previous post, we have looked at evaluating the robustness of a model for making predictions on unseen data using cross-validation and multiple cross-validation where we used classification accuracy and average classification accuracy. Once you have a […]

Last Updated on June 7, 2016 Phil Brierley won the Heritage Health Prize Kaggle machine learning competition. Phil was trained as a mechanical engineer and has a background in data mining with his company Tiberius Data Mining. He is heavily into R these days and keeps a blog at Another Data Mining Blog. In October 2013 he presented to the Melbourne Users of R special interest group. The title of his talk was “Techniques to improve the accuracy of your Predictive Models” and you can […]

Last Updated on August 16, 2020 Machine Learning has been commoditized into a service. This is a recent trend that looks like it will develop into the mainstream like commoditized storage and virtualization. It is the natural next step. In this review you will learn about BigML that provides commoditized machine learning as a service for business analysts and application integration. About BigML BigML was co-founded by a group of five guys in 2011. Francisco Martin seems to be active […]

Last Updated on June 7, 2016 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 tutorial we will walk through a step-by-step tutorial on developing a predictive model using the BigML platform and use […]

Last Updated on April 4, 2018 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 disproportionate amount of time thinking about algorithm tuning and running tuning experiments. I am prone to performing post-competition analysis […]

Last Updated on June 7, 2016 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 in the layout and management of your machine learning project. This is very much related to the goals of […]

Last Updated on August 15, 2020 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 post you will discover and consider this important trade-off. Model Accuracy vs ExplainabilityPhoto by Donald Hobern, some rights reserved […]