I've had a lot of requests, so here they are. Hopefully, all of the slides will be posted on the conference website.

I've had a lot of requests, so here they are. Hopefully, all of the slides will be posted on the conference website.

The conference was excellent this year. My highlights: Bojan Mihaljevic gave a great presentation on machine learning models built from network models. Their package isn't on CRAN yet, but I'm really looking forward to it. Jim Harner's presentation ...

In Chapter 18, we discuss a relatively new method for measuring predictor importance called the maximal information coefficient (MIC). The original paper is by Reshef at al (2011). A summary of the initial reactions to the MIC are Speed and Tibshirani (and others can be found here). My (minor) beef with it is the lack...

"Bees don't swarm in a mango grove for nothing. Where can you see a wisp of smoke without a fire?" - Hla Stavhana In the last two posts, genetic algorithms were used as feature wrappers to search for more effective subsets of predictors. Here, I will do the same with another type of search algorithm: particle swarm optimization....

Here is a summary of some recent changes to caret. Feature Updates: train was updated to utilize recent changes in the gbm package that allow for boosting with three or more classes (via the multinomial distribution) The Yeo-Johnson power transformation was added. This is very similar to the Box-Cox transformation, but it does not require the data to be...

I've been looking at this article for a new tree-based method. It uses other classification methods (e.g. LDA) to find a single variable use in the split and builds a tree in that manner. The subtleties of the model are: The model does not prune but ...

Previously, I talked about genetic algorithms (GA) for feature selection and illustrated the algorithm using a modified version of the GA R package and simulated data. The data were simulated with 200 non-informative predictors and 12 linear effects and three non-linear effects. Quadratic discriminant analysis (QDA) was used to model the data. The last set of...

In the feature selection chapter, we describe several search procedures ("wrappers") that can be used to optimize the number of predictors. Some techniques were described in more detail than others. Although we do describe genetic algorithms and how they can be used for reducing the dimensions of the data, this is the first of series of blog posts that...

What is the objective of most data analysis? One way I think about it is that we are trying to discover or approximate what is really going on in our data (and in general, nature). However, I occasionally run into people think that if one model fulfills our expectations (e.g. higher number of significant p-values or accuracy) than it...