How does google prediction API work [closed]

Question

Can someone predict :) or guess how does the Google Prediction API work under the hood? I know there are some machine learning techniques: Decision Trees, Neuron networks, naive Bayesian classification etc.

Which technique do you think Google is using?

Answer 1

The single answer to the question on Stats SE is good, given limited information from Google itself. It concludes with the same thought I had, that Google isn't telling regarding the innards of the Google Prediction API.

There was a Reddit discussion about this too. The most helpful response was from a user who is credible due to his prior work in that field (in my opinion). He wasn't certain what Google Prediction API was using, but had some ideas about what it was NOT using, based on discussions on the Google Group for the Prediction API:

the current implementation is not able to deal correctly with non-linear separable data sets (XOR and Circular). That probably means that they are fitting linear models such as regularized logistic regression or SVMs but not neural networks or kernel SVMs. Fitting linear models is very scalable to both wide problems (many features) and long problems (many samples) provided that you use... stochastic gradient descent with truncated gradients to handle sparsity inducing regularizers.

There was a little more, and of course, some other responses. Note that Google Prediction API has since released a new version, but it is not any more obvious (to me) how it works "under the hood".