The three benchmark algorithms for predicting the location of dark matter halos are, for the most part, all over the map. Most of the test skies look something like this: There are, however, some skies with rather strong halo signals that get a decent amount of agreement: The Lenstool MLE ... [Read more...]

Some people like to do crossword puzzles. I like to do machine learning puzzles. Lucky for me, a new contest was just posted yesterday on Kaggle. So naturally, my lazy Saturday was spent getting elbow deep into the data. The training set consists of a series of ‘skies’, each containing ... [Read more...]

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If you haven’t yet discovered the competitive machine learning site kaggle.com, please do so now. I’ll wait. Great – so, you checked it out, fell in love and have made it back. I recently downloaded the data for the getting started competition. It consists of 42000 labelled images (28×28) of ... [Read more...]

I took part in the second Data Science London Hackathon last weekend (also my second hackathon) and it was a very different experience compared to the first hackathon. Once again Carlos and his team really looked after us. The data was released 24 hours before the competition started and even though ... [Read more...]

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There are quite a few books out now on “data science”. I’ve picked out three that I think are the best place to start for computational journalists. First is Machine Learning for Hackers, by Drew Conway and John Myles White. The autho... [Read more...]

I describe a kernel density approach to outlier detection on small datasets. In particular, my model is the set of prices for a given item that can be found online. Introduction Suppose you’re searching online for the cheapest place to … Continue reading → [Read more...]

In my previous post I showed you how to randomly split up a dataset into training and testing datasets. (Thanks to all those who emailed me or left comments letting me know that this could be done using other means. As things go with R, it's sometimes ... [Read more...]

I recently analyzed some data trying to find a model that would explain body fat distribution as predicted by several blood biomarkers. I had more predictors than samples (p__n), and I didn't have a clue which variables, interactions, or quadratic terms made biological sense to put into a model. ... [Read more...]

There are a number of good open source projects for statistics and data mining, for example the software WEKA developed at the University of Waikato. The description on their website states that: Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied ... [Read more...]

In the wake of the main machine learning NIPS 2010 meeting in Vancouver, Dec. 6-9 2010, there will be a very interesting workshop organised by Ryan Adams, Mark Girolami, and Iain Murray on Monte Carlo Methods for Bayesian Inference in Modern Day Applications, on Dec. 10. (And in Whistler, not Vancouver!) I wish ... [Read more...]

The bottom line of this post is for you to go to: Stack Exchange Q&A site proposal: Statistical Analysis And commit yourself to using the website for asking and answering questions. (And also consider giving the contender, MetaOptimize a visit) * * * * Statistical analysis Q&A website is about to go ... [Read more...]

The authors here invited ACM KDD Innovation Award and IEEE ICDM Research Contributions Award winners to each nominate up to 10 best-known algorithms in data mining, including the algorithm name, justification for nomination, and a representative public... [Read more...]

Download "Getting Started with the Social Media Analytics Research Toolkit" (pdf, 1.25 megabytes) Download the Social Media Analytics Research Toolkit My Social Media Analytics Research Toolkit is about to move into private beta. What's in the release?... [Read more...]

Conditional inference trees (ctree) in package party allows weighting which is useful when one classification outcome is more important than another. Useful examples are not difficult to imagine: in a marketing direct mailing, a false positive (non-res... [Read more...]

This tutorial demonstrates to the R novice how to create five machine learning models for classification and compare the performance graphically with ROC curves in one chart. For a simpler introduction, start with Plot ROC curve and lift chart in R. # ... [Read more...]

This tutorial with real R code demonstrates how to create a predictive model using cforest (Breiman’s random forests) from the package party, evaluate the predictive model on a separate set of data, and then plot the performance using ROC curves ... [Read more...]

A Beautiful WWW put together a great set of resources for getting started with machine learning in R.  First, they recommend the previously mentioned free book, The Elements of Statistical Learning.  Then there's a link to a list of dozens of machine learning and statistical learning packages for R.  Next, ... [Read more...]