How to Run Your First Classifier in Weka

Last Updated on August 22, 2019 Weka makes learning applied machine learning easy, efficient, and fun. It is a GUI tool that allows you to load datasets, run algorithms and design and run experiments with results statistically robust enough to publish. I recommend Weka to beginners in machine learning because it lets them focus on learning the process of applied machine learning rather than getting bogged down by the mathematics and the programming — those can come later. In this post, I want […]

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How To Choose The Right Test Options When Evaluating Machine Learning Algorithms

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 […]

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Design and Run your First Experiment in Weka

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 […]

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Quick and Dirty Data Analysis for your Machine Learning Problem

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 […]

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How to Tune a Machine Learning Algorithm in Weka

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 […]

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Make Better Predictions with Boosting, Bagging and Blending Ensembles in Weka

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 […]

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4-Steps to Get Started in Applied Machine Learning

Last Updated on August 16, 2020 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 that you used to learn how to program. Namely, get the basics, practice a lot and dive into the […]

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A Simple Intuition for Overfitting, or Why Testing on Training Data is a Bad Idea

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 […]

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Template for Working through Machine Learning Problems in Weka

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 […]

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Biggest Mistake I Made When Starting Machine Learning, And How To Avoid It

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 […]

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