Eric Siegel’s Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die is a nontechnical overview of modern analytics with detailed discussion of how machine learning is being deployed across all industries and in all major corporations. Eric is a hugely entertaining writer and brings with him the expertise you would expect of a Columbia University trained Ph.D.. Geoffrey Moore writes that the book is “deeply informative” and Tom Peters calls the book “The most readable ‘big data’ book I’ve come across. By far”.Read More
We recently came across the article, "Random Forest---the go-to machine learning algorithm" from TechWorld Australia.Read More
Probabilities in CART trees are quite straightforward and are displayed for every node in the CART navigator. Below we show a simple example from the KDD Cup ‘98 data predicting response to a direct mail marketing campaign.
There are several tricks available for maneuvering CART into generating a single tree structure that will output predictions for several different target (dependent) variables in each terminal node. For CART the idea seems very natural in that the structure of the model is just a segmentation of the data into mutually exclusive and collectively exhaustive segments. If the segments of CART tree designed for one target variable have been well constructed then the segments could easily be relevant for the prediction of many outcomes. A segmentation (CART tree) based on common demographics and Facebook likes, for example, could be used to predict consumption of tuna fish, frequency of cinema visits, and monthly hair stylist spend. Of course, the question is: could a common segmentation in fact be useful for three such diverse behaviors, and, if such a segmentation existed, would be able to find it?