A neuron is a single node within a neural network. By analogy with neurons within the brain we can think of a neuron “firing” in response to an input trigger, and we can think of machine learning as the process of training the neuron to recognise that input trigger.

# Tag Archives: Predictive Modelling

# 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.

# 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.

# 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.

# Fives and Sixes

In my last post I was able to successfully re-orient a set of pixel data to reconstruct images of handwritten digits. SInce version 12 of JMP we have been able to create *expression* table columns that can contain images. That’s a logical location to store my newly revealed images:

# Performance Profiling

In my last post I illustrated the performance boost generated by using matrix operations to conduct least squares regression calculations. Matrices by their nature require numerical data. So what about handling a categorical predictor variable? To do this it’s necessary to create dummy variables – separate variables for each unique level of the predictor variable.

# Regression in Matrix Form

I’m working on some predictive modelling projects and I need to iteratively compute R^{2} statistics over 100’s of variables. Each time I do the calculations I need to go and have an extended coffee break – and I’m starting to buzz with too much caffeine so I thought I would look to see whether I could make my code more efficient!