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 […]
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 […]
This is a project spotlight with Artem Yankov. Could you please introduce yourself? My name is Artem Yankov, I have worked as a software engineer for Badgeville for the last 3 years. I’m using there Ruby and Scala although my prior background includes use of various languages such as: Assembly, C/C++, Python, Clojure and JS. I […]
The promise of Data Mining was that algorithms would crunch data and find interesting patterns that you could exploit in your business. The exemplar of this promise is market basket analysis (Wikipedia calls it affinity analysis). Given a pile of transactional records, discover interesting purchasing patterns that could be exploited in the store, such as offers […]
This is a project spotlight with Konstantin Slisenko a programmer and machine learning enthusiast. Could you please introduce yourself? My name is Konstantin Slisenko, I’m from Belarus. I graduated from the Belarusian State University of Informatics and Radioelectronics. I am currently taking a master course. I’m a Java developer and work in JazzTeam company. I like […]
Working on a problem, you are always looking to get the most out of the data that you have available. You want the best accuracy you can get. Typically, the biggest wins are in better understanding the problem you are solving. This is why I stress you spend so much time up front defining your […]
When I first got started in machine learning I implemented algorithms by hand. It was really slow going. I was a terrible programmer at the time. I was trying to figure out the algorithms from books, how to use them on problems and how to write code – all at the same time. This was […]
When you are getting started in Weka, you may feel overwhelmed. There are so many datasets, so many filters and so many algorithms to choose from. There is too much choice. There are too many things you could be doing. Structured process is key. I have talked about process and the need for tasks like […]
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 […]
A Top-Down Strategy for Beginners to Start and Practice Machine Learning. Getting started is much easier than you think. In this post I show you the top-down approach for getting started in applied machine learning. You will discover the four steps to this approach. They should feel familiar because it’s probably the same top-down approach […]
