Claim an sklearn algorithm to implement and troubleshoot

[dhimmel]

In the August 26 meetup, we discussed having each team member in the machine learning group claim an algorithm. We've made lot's of progress on the example notebook (1.TCGA-MLexample.ipynb) since then (see #18 & #25). Currently, 1.TCGA-MLexample.ipynb uses elastic net logistic regression implemented in SGDClassifier.

The goal of this repository is for people to:

1. Claim an algorithm. See the list of classifiers at https://github.com/cognoma/machine-learning/issues/5#issuecomment-235069679. The main requirement is that the algorithm uses the sklearn API so we can use it in the pipeline. Make a comment here once you've chosen an algorithm.
2. Create a modified version of 1.TCGA-MLexample.ipynb in an algorithms directory. So if I took the SVM classifier, I would copy 1.TCGA-MLexample.ipynb to algorithms/SVC-dhimmel.ipynb. Then I would make my edits to algorithms/SVC-dhimmel.ipynb to switch to an SVC classifier.
3. Your goal should be to pick a good set of parameters for grid search. It would also be great if you could document what seems to work well about the algorithm (or if it doesn't seem to work well).

Best of luck! If you can work on this before the August 9 meetup then great! Otherwise make sure to bring a laptop with the cognoma-machine-learning environment installed.