Closed jknowles closed 9 years ago
Errr. Don't merge this in. I am realizing we have a bit of an issue with how to handle missing predictions all the way down the stack of functions.
I'm currently diagnosing this and seeing what solution we might want to come up with for the short term and the long term.
Sounds good!
@jknowles Once you've fixed the missing data issue, we can re-visit this.
Here's another idea: once of the caretEnsemble graphs should be a plot of the model's error vs. iteration number. If the error is increasing, the user can tell somethings gone wrong with the optimization.
The build is passing, but NA resolution is still not transparent to the user. I am going to add a few more tests and push them to this branch. The optimizers are performing correctly, but they are all using na.rm=TRUE
with no user option to do anything else and no warning about missing values.
Just let me know when you think this is ready to merge!
I think if this build passes the Travis CI check, it should be ready to merge.
Looks pretty good to me. I have a few comments prior to merging (I just got back from vacation, so sorry it took a while for me to get around to this!)
OK -- I cleaned up the style and undid my change to optRMSE. We should be set.
Note that this branch makes optRMSE behave like optAUC in that model weights are optimized based on cases which data is available across all models in the library. Previously, AUC did this by default through the colAUC function, but now the RMSE optimizer behavior does the same thing. No warning or message is given to the user, but I noted this in the documentation for caretEnsemble
and the optimizer functions.
This closes a couple remaining issues for v. 1.0 I think too.
Sounds good. I think making them consistent makes sense, and we can decide how to better deal with NA
s later.
Great. Thanks for reviewing this -- no problem with the vacation! We are very close to a 1.0 release now!
One more issue to fix!!
Adds tests for
optAUC
functions andoptRMSE
functions. ChangessafeOptAUC
andgreedOptAUC
to issue a warning and a message respectively in case of optimization underperforming a component model. In the case ofsafeOptAUC
, the best model is weighted 1 and returned, ingreedOptAUC
the best optimization is returned with a message.Basic tests for greedOptRMSE and ability to issue a message in degenerate cases. However, no tests for degenerate cases are included because I couldn't generate any data to do this.
Closes #75.