Has anyone here put LC data through Regression Trees? I just came across this stuff on a work assignment and they used it on a portfolio of credit to find advance relationships that would be too difficult to detect without big data technology. I'm considering signing up for the free trial and running LC data through it to see what it shows but was curious if anyone here had done it or had experience with it. And yes, I'm looking at you Fred, AnilG, and BryceMason.
Examples:
http://www.mu-sigma.com/http://www.salford-systems.com/http://www.angoss.com/
I have always thought that a neural net was the best way to build loan prediction systems.
http://www.r-bloggers.com/using-neural-networks-for-credit-scoring-a-simple-example/
PeerCube uses Decision Tree/Regression Tree for splitting the loan attributes before calculating BLE Risk Index. If you are familiar with R, you can use it with 'party' or 'rpart' packages. As the example below and attached shows, even with three loan attributes and ROI, the analysis gets complex.
Personally, I suggest lenders look into Genetic Algorithm as described by David M. Patierno
http://blog.dmpatierno.com/post/3161338411/lending-club-genetic-algorithm. PeerCube has it as Public Loan Filter 'DMP Genetic Algorithm'.
It can work well, but I'm not a fan. I prefer to understand the reasons why we see relationships, and the computer finding the best way to slide down regression trees / random forests like a game of Plinko on the Price is Right just doesn't satisfy my curiosity. There are many ways to skin a cat.
That's a fine approach to improve models, but one better use a very high alpha because in my book this is a fishing expedition. You check every permutation with a genetic or tree and there are bound to be (1-alpha)% variables significant just by chance. So, back testing and high alpha are important here IMO.
even i think that a neural net was the best way to build loan prediction systems
example:
https://www.nuncsystems.com/big-data.html