I was recently asked a question about updating display boxes. Display boxes are the building blocks of JMP output windows. Fundamentally there are two methods of updating these display boxes, which I will take a closer look at. (more…)

# Segmented Regression

I’m sure there is a more technically correct term for this: I use the phrase *segmented regression* to describe the process whereby I select a segment of data within a curve and build a regression model for just that segment.

I have some code to aid the process. The code illustrates how to perform regression on-the-fly as well as how to utilise the *MouseTrap* function to handle mouse movement events.

# Model Diagnostics Addin

# Easter Egg

An Easter egg is an intentional inside joke, a hidden message, or a secret feature of an interactive work (often, a computer program, video game or DVD menu screen). The name is used to evoke the idea of a traditional Easter egg hunt

– Wiki

The above visualisation is a 3D tree view of a decision tree generated with the Partition platform.

However, if you look under the red triangle hotspot for the platform you won’t find an option to create this output.

# A Trivial Neural Network

I have been investigating the use of logistic regression to model image pixel data. Now I want to take a look at the use of neural networks. In this post I am going to build the simplest possible neural network and compare it against a simple logistic regression.

# Web Scraping TripAdvisor

Since writing this post I have placed the associated code on the

JMP File Exchange …

The problem with the internet is that it gives you too much information, or rather, it takes too long to gather the information. I often cross reference hotel booking sites with TripAdvisor, and its a laborious process. So this evening I decided to streamline my process by writing a script to gather to user reviews into a JMP table and simple report.

# Stepping Carefully

In this post I will continue with my so-called hieroglyphics project. This project uses a set of image data that describes handwritten characters. The dataset is frequently used to evaluate machine-learning algorithms. I’m using the dataset to explore a variety of modelling techniques within JMP.

In my last post I used a script to incrementally add terms to my model so that I could explore the performance of the model with increasing complexity. But the order in which I added the terms was based on a heuristic and it wasn’t necessarily optimal. So in this post I want to explore using stepwise regression.

# Real-Time Data Capture

JMP is brilliant for real-time data capture. Add to that the ability to use JSL to construct “industrial” style user-interfaces and its easy to get JMP deployed in an environment that relies on simple to use robust data capture from online measurement systems.

# Logistic Regression pt. 2

# Logistic Regression pt.1

In a recent post I created a table that contained two classes of data: images that represent either the handwritten digit ‘5’ or the digit ‘6’. In this post I’ll model the data using logistic regression. I will also take the opportunity to look at the role of training and test datasets, and to highlight the distinction between testing and validation.