In a previous post I discussed k-means clustering, which is a type of unsupervised learning method.  Today I want to add another tool to our modeling kit by discussing hierarchical clustering methods and their implementation in R. As in the k-means clustering post I will discuss the issue of clustering countries based on macro data.  … More Hierarchical Clustering

In my last post I discussed the importance of using a regularized classifier to avoid overfitting the data. I used contour plots to show the distortions in the decision boundaries of a logistic regression classifier on artificial data. Recently I have been going through Nigel Lewis’ “Build Your Own Neural Network Today!” and decided to … More Regularized Neural Network in R

I recently went through a Coursera course on Classification taught by Carlos Guestrin from U of Washington and thought it was excellent.   There was an interesting discussion on model overfitting that I thought I would share. In previous posts I discussed linear models with shrinkage parameters such us ridge and lasso regression models. Similar approach … More Shaving a Classifier with Occam’s Razor

My posts have been spars as I adjust being back home. Today’s post will be about a somewhat gimmicky approach to visualizing correlation in the markets. A while back I came across an interesting article by Resovsky et al on minimum spanning trees. I do not have a background in graph theory but I have … More Minimum Spanning Trees