Last Updated on August 22, 2019 Weka is the perfect platform for learning machine learning. It provides a graphical user interface for exploring and experimenting with machine learning algorithms on datasets, without you having to worry about the mathematics or the programming. A powerful feature of Weka is the Weka Experimenter interface. Unlike the Weka Explorer that is for filtering data and trying out different algorithms, the Experimenter is for designing and running experiments. The experimental results it produces are […]

Last Updated on August 22, 2019 A part of having a good understanding of the machine learning problem that you’re working on, you need to know the data intimately. I personally find this step onerous sometimes and just want to get on with defining my test harness, but I know it always flushes out interested ideas and assumptions to test. As such, I use a step-by-step process to capture a minimum number of observations about the actual dataset before moving […]

Last Updated on August 22, 2019 Weka is the perfect platform for learning machine learning. It provides a graphical user interface for exploring and experimenting with machine learning algorithms on datasets, without you having to worry about the mathematics or the programming. In a previous post we looked at how to design and run an experiment with 3 algorithms on a dataset and how to analyse and report the results. Manhattan Skyline, because we are going to be looking at […]

Last Updated on August 22, 2019 Weka is the perfect platform for studying machine learning. It provides a graphical user interface for exploring and experimenting with machine learning algorithms on datasets, without you having to worry about the mathematics or the programming. In a previous post we looked at how to design and run an experiment running 3 algorithms on a dataset and how to analyse and report the results. We also looked at how to design and run an […]

Last Updated on August 22, 2019 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. Too much ChoicePhoto by emilio labrador, some rights reserved. Structured process is key. I have talked about process and the need for tasks like spot checking algorithms to overcome the overwhelm and start learning […]

Last Updated on August 22, 2019 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 the biggest mistake I made when getting started. It made everything 3-times harder and killed my motivation. A friend […]

Last Updated on August 16, 2020 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 problem, analyzing the data, and preparing datasets for your models. A key part of data preparation is creating transforms […]