
Recent Posts
Recent Comments
ct98.aspx on Random forests Paul on Kernels Iliana Doggett on Kernels Hyperspectral Imagin… on Gaussian kernels nikhil on Kmodes Archives
 December 2016
 November 2016
 October 2016
 June 2016
 April 2016
 January 2016
 November 2015
 October 2015
 July 2015
 June 2015
 May 2015
 January 2015
 September 2014
 June 2014
 May 2014
 March 2014
 February 2014
 January 2014
 December 2013
 October 2013
 September 2013
 August 2013
 July 2013
 June 2013
 May 2013
 April 2013
 March 2013
Categories
Meta
Category Archives: Modeling
Continuous Bayes’ Theorem
Bayes’ Rule is one of the fundamental Theorems of statistics, but up until recently, I have to admit, I was never very impressed with it. Bayes’ gives you a way of determining the probability that a given event will occur, or … Continue reading
Posted in Modeling
2 Comments
Genetic algorithms and symbolic regression
A few months ago, I wrote a post about optimization using gradient descent, which involves searching for a model that best meets certain criteria by repeatedly making adjustments that improve things a little bit at a time. In many situations, this works … Continue reading
Posted in Modeling, Regression
Leave a comment
Configuration Spaces and the Meaning of Probability
I recently finished reading Nate Silver’s book The Signal and the Noise, which has gotten me thinking about how exactly one should interpret models/probability distributions, and the predictions they make. (If you’ve read this book or plan to read it, … Continue reading
Posted in Modeling
8 Comments
Kmeans
The subject of this weeks post is probably one of the most polarizing algorithms in the data world: It seems that most experts either swear by Kmeans or absolutely hate it. The difference of opinion boils down to one of … Continue reading
Posted in Modeling, Unsupervised learning
8 Comments
Mixture models
In the last few posts, we’ve been looking at algorithms that combine a number of simple models/distributions to form a single more complex and sophisticated model. With both neural networks and decision trees/random forests, we were interested in the classification … Continue reading
Posted in Modeling
7 Comments
Principal Component Analysis
Now that we’ve gotten a taste of the curse of dimensionality, lets look at another potential problem with the basic form of regression we discussed a few posts back. Notice that linear/least squares regression always gives you an answer, whether or … Continue reading
Posted in Modeling
22 Comments
The curse of dimensionality
Now that we’ve had a glimpse of what it means to analyze data sets in different dimensions, we should take a little detour to consider really high dimensional data. In the discussion of regression, I suggested using your intuition about … Continue reading
Posted in Modeling
10 Comments