The Google Prediction API is a black-box system for building predictive models, it provides pattern-matching and machine learning capabilities. So Google algorithms automatically creates a model from the training models given a set of training data and makes prediction under this model given a set of explanatory variables, read http://code.google.com/apis/predict/docs/getting-started.html for an overview.

I am eager to make my hands dirty after the release of R client library for the Google Prediction API @ http://code.google.com/p/google-prediction-api-r-client/, however, as both my office’s computer and my laptop are under Windows, there are few issues for the R API package:
1, the original usage example had mistake. I always had a problem when I wanted to train my local data, my.model <- PredictionApiTrain(data="MYPATH/MYFILE.csv"), it says
Error in PredictionApiTrain(data = “test.csv”) : ‘remote.file’ should be character.
At the beginning of PredictionApiTrain() there are lines:

However, remote.file is an argument without default value, function (data, remote.file, verbose = FALSE). Now the example has been changed to my.model <- PredictionApiTrain (data="MYPATH/MYFILE.csv",remote.file="gs://MYBUCKET/MYOBJECT");

2, even when I use PredictionApiTrain
(data=”MYPATH/MYFILE.csv”,remote.file=”gs://MYBUCKET/MYOBJECT”),
trained model my.model is expected, however, R returns
Error in system(command = command, intern = TRUE) : gsutil not found
I did install gsutil as described at http://code.google.com/apis/storage/docs/gsutil.html, and can list my bucket using: python gsutil ls gs://MYBUCKET/MYOBJECT.
The comment I got this afternoon is:

It seems we have to wait for a while before testing this promising application, luckily the ball is moving forward. Please share with us if you find a way out. I am looking forward to it and will update once I successfully try CygWin.
Tags – r , prediction
Read the full post at Issues of R Client Library For The Google Prediction API.