Why you should be Spot-Checking Algorithms on your Machine Learning Problems

Last Updated on August 16, 2020 Spot-checking algorithms is about getting a quick assessment of a bunch of different algorithms on your machine learning problem so that you know what algorithms to focus on and what to discard. Photo by withassociates, some rights reserved In this post you will discover the 3 benefits of spot-checking algorithms, 5 tips for spot-checking on your next problem and the top 10 most popular data mining algorithms that you could use in your suite […]

September 29, 2020 Machine Learning

Applied Machine Learning Process

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 […]

September 29, 2020 Machine Learning

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 […]

September 29, 2020 Machine Learning

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 […]

September 29, 2020 Machine Learning

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 […]

September 29, 2020 Machine Learning

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 […]

September 29, 2020 Machine Learning

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 […]

September 29, 2020 Machine Learning

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 […]

September 29, 2020 Machine Learning

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 […]

September 29, 2020 Machine Learning

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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