Where is the “learning” in machine learning? The problem with machine learning algorithms is that they yield a final solution without giving you a sense of the learning process. So I’ve implemented an interactive version of a perceptron learning algorithm. To run the algorithm I need lenearly separable data – hence the posts from the previous weeks. Now I can use that as the basis for visualising the machine learning steps. (more…)
All posts by David Burnham
Visualising Machine Learning pt.1
My last two posts have been talking creating linearly separable data. Hopefully you found some of the features interesting – for example, the idea of creating interactivity using grab handles. But I never really gave a purpose to what I was doing: I wanted to write some machine learning code – specifically a perceptron learning algorithm – and to test the code I needed to create some data on which it could act. (more…)
Grab Those Handles!
I have a graph with a line drawn on it. I want to user to be able to change the orientation of the line by “clicking and dragging”. To do this need to implement drag-handles. Here’s how … (more…)
Linearly Separable Data
In my last post I outlined some “homework” that I had set myself – to write a script that would create linearly separable data. I want the ability to create it in an interactive environment. (more…)
Some Homework
Don’t worry. The homework is for me, not you. But feel free to have a go yourself also! I want to create a set of data which contains a binary response that is linearly separable within a plane defined by two input variables. (more…)
Finding That Line Of Code
Here’s the problem. I have a line of code that I want to locate containing the words:
Rank Index
Here’s the problem. I have a list of ‘things’, for example, batch names. I can also get another list, for example, the start dates of the batches. How do I sort the batches by date? The answer: use the Rank Index function. (more…)
Creating A JMP Add-In
I’m going to take a look at the process of creating a JMP add-in to create a single-file deployment package for a collection of files associated with a JMP script. (more…)
Box-Jenkins, and JMP
There are two main classes of model for time series data – autoregressive (AR) and moving average (MA). The generalisation of the two is referred to as ARIMA – autoregressive integrated moving average. These models are sometimes referred to as Box-Jenkins models, but more accurately the term “Box-Jenkins” refers to a methodology for model selection. (more…)
Linear Programming
Did you know that JMP has an LP Solver? Linear programming (LP) is a technique for optimising a function subject to a set of linear constraints. (more…)