It turns out that the prediction profiler has a hidden secret. And not just some easter egg feature that is just a bit of fun. This secret is core to how you use the profiler – and might just totally change how you use it in future.

# Category Archives: Modelling

# The Neuron

# Prepping Data

Most of the work associated with building a predictive model is associated with either performance tuning or data prepping.

I’m almost half way through prepping some data. It’s not necessary to script this but a script allows me to adjust the data preparation in the future and more importantly to document the sequence of steps that I have taken.

# 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

# A Trivial Neural Network

# 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.

# 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.