generate GLM binomial predict using the data used to train the model. Download the predict to csv file. Expect error rate in downloaded csv of predict.hex when compared to output col of original data, to be similar to the error rate reported during training? n_folds=1.
tried exact same test with just a different dataset (10% sample of covtype) so less rows. passes with 23.9% training error and 23.9% error comparing the downloaded predict to the original output col in the source csv file (this compare is done in python, reading the two csvs)
same test, using iris, covtype 10pct, and covtype (only covtype fails) (rf/predict/download csv/compare to original data expecting error rate similar to training error)
forgot to rm this (superseded)
add use_non_local_data=1 so the rf model will be roughly as good, if I use 3 jvms
add -ccj command line arg and clone_cloud= params as alternate ways of starting a cloud clone (command line can be used to override a test that normally builds a cloud, so all tests can run as-is. Also, michal could pass a clone_cloud param in the ec2 config.json, so that they can do that on ec2/jenkins also (you can't use command line args with nosetests. But a clone_cloud config bit, like the s3n import folder redirect bits, must be possible/manageable
Merge remote-tracking branch: upstream/master