We all know the satisfaction of running an analysis and seeing the results come back the way we want them to: 80% accuracy; 85%; 90%? The temptation is strong just to turn to the Results section of the report we’re writing, and put the numbers in. But wait: as always, it’s not that straightforward. Succumbing […]
I get a lot of questions about machine learning via email and I love answering them. I get to see what real people are doing and help to make a difference. (Do you have a question about machine learning? Contact me). In this post I highlight a few of the interesting questions I have received […]
It is easy to feel overwhelmed with the large numbers of machine learning algorithms. There are so many to choose from, it is hard to know where to start and what to try. The choice can be paralyzing. You need to get over this fear and start. There is no magic book or course that […]
In my courses and guides, I teach the preparation of a baseline result before diving into spot checking algorithms. A student of mine recently asked: If a baseline is not calculated for a problem, will it make the results of other algorithms questionable? He went on to ask: If other algorithms do not give better accuracy […]
Any book on machine learning will list and describe dozens of machine learning algorithms. Once you start using tools and libraries you will discover dozens more. This can really wear you down, if you think you need to know about every possible algorithm out there. A simple trick to tackle this feeling and take some […]
Edward Raff is the author of the Java Machine Learning library called JSAT (which is an acronym for Java Statistical Analysis Tool). Edward has implemented many algorithms in creating this library and I recently reached out to him and asked what advice he could give to beginners implementing machine learning algorithms from scratch. In this post […]
Machine learning algorithms are complex systems that require study to understand. Static descriptions of machine learning algorithms are a good starting point, but are insufficient to get a feeling for how the algorithm behaves. You need to see the algorithm in action. Experimenting on a running machine learning algorithms will allow you to build an […]
Algorithms are a big part of the field of machine learning. You need to understand what algorithms are out there, and how to use them effectively. An easy way to shortcut this knowledge is to review what is already known about an algorithm, to research it. In this post you will discover the importance of […]
After spot checking algorithms on your problem and tuning the better few, you ultimately need to select one or two best models with which to proceed. This problem is called model selection and can be vexing because you need to make a choice given incomplete information. This is where the test harness you create and […]
Algorithms make up a big part of machine learning. You select and apply machine learning algorithms to build a model from your data, select features, combine the predictions from multiple models and even evaluate the capabilities of a given model. In this post you will review 5 different approaches that you can use to study […]
