Characterize predictive performance of each ML algorithm in our toolbox vs various data sets for a representative set of queries

[ejsegall]

Issue #5 describes a table of ML methods. (This is the "toolbox" referred to in the title)
Issue #11 describes creating a set of sample data sets.
This issue is a task that consists of:

• Running each algorithm on each data set and evaluating its predictive quality.
• To the best of your capability, provide a few words explaining the results

Since there are a lot of algorithms and a lot of datasets there is an opportunity for many people to participate in this task.

@dhimmel

[ejsegall]

sameertipnis@gmail.com is interested in participating

[RenasonceGent]

I will work on a plug and play architecture for the algorithms.

[ejsegall]

@RenasonceGent: please see issue #13