Google Prediction API 1.5 adds enumeration, analysis, and more

By Marc Cohen, Developer Relations

The Google Prediction API Team has been hard at work on Release 1.5, which is available now, with the following new features:

• Model enumeration. We’ve added the ability to list all of your models via the trainedmodels.list request. You can obtain the entire list in one response or you can iterate through a large listing in pieces using the maxResults and pageToken options.


• Model analysis. We’ve added the ability to obtain more detailed information about data and models via the trainedmodels.analyze request, which returns information about the trained model’s output values, features, confusion matrix, and other information.


• Simplified get method. We’ve simplified the output returned by the trainedmodels.get request. Model analysis data that previously was returned by a get request (e.g. the confusion matrix), is now returned by the new analyze request, along with additional analysis data. The get response now returns a simpler model description along with new timestamps indicating when the model was inserted and when model training completed, which should make it easier to keep track of model lifecycle.


• New Google App Engine samples. We’ve created two new sample apps illustrating how to use the Prediction API from App Engine, coded in Python and Java. These samples show how to create and manage shared server OAuth 2.0 credentials, and how to make predictions on behalf of any site visitors using the shared server credentials. The sample code is available here and a live version of the sample app is available here: http://try-prediction.appspot.com.

You can read more about the API details here. The new release is available now via the HTTP RESTful interface and our various language-specific client libraries. You can also experiment with the new Prediction API 1.5 interactively via the Google APIs Explorer.

We’re always looking for ways to improve the Prediction API so, as always, please let us know about any problems or feature suggestions you might have. Happy Predicting!


Marc Cohen is a member of Google’s Developer Relations Team in Seattle. When not teaching Python programming and listening to indie rock music, he enjoys using the Google Prediction API to peer into the future.

Posted by Scott Knaster, Editor