Would you give every data point of yours over to Google?
Google Prediction API
11–20 of 88 posts
Re: Google Prediction API
#12Would you give every data point of yours over to Google?
Data doesn't have to be readable. Often, preprocessed datasets are totally incomprehensible for everyone except whoever prepared it. Multiple fields are combined into composites, rescaled, transposed, etc.
Re: Google Prediction API
#13I predict this will be a hit.
Re: Google Prediction API
#14Are there any (preferably FOSS) libraries that does anything like this?
In Python there is a wonderful library called the Natural Language Toolkit (NLTK) available free and open source at http://www.nltk.org/ . With NLTK you can build classifiers, decision trees, and train/predict with bayesian classifiers similarly to Google's Prediction API examples. It's pretty easy to get started, and it's code that you run locally, so there is no network traffic. I use it on http://www.protopub.com…
There's also Weka, which can use almost exactly the same file format that Google is using, and do the same kind of things (though perhaps with different algorithms). It's pretty pleasant.
Re: Google Prediction API
#15"Upload your data to Google Storage for Developers, then use the Prediction API to make real-time decisions in your applications."
I can understand the necessity of this, but that'll be some serious lock-in.
Re: Google Prediction API
#16From the very little information that I see available so far, it appears that Google will first stab at discrete predictions. That is, I don't see probabilistic output yet.
Also, from http://code.google.com/apis/predict/docs/developer-guide.htm..., it is clear that they perform accuracy analysis using the training data. That is, there is no "testing" vs "training" dataset distinction at this point; there is just cross-validation of the training set.
Re: Google Prediction API
#17Re: Google Prediction API
#18From the very little information that I see available so far, it appears that Google will first stab at discrete predictions. That is, I don't see probabilistic output yet. Also, from http://code.google.com/apis/predict/docs/developer-guide.htm... , it is clear that they perform accuracy analysis using the training data. That is, there is no "testing" vs "training" dataset distinction at this point; there is just cro…
> That is, there is no "testing" vs "training" dataset distinction at this point; there is just cross-validation of the training set.
If they just create a test set from the training set, and omit that from the training, what's the difference? The main thing is that you don't want to include the test set in the training step, and I assume they're doing that.
Re: Google Prediction API
#19Re: Google Prediction API
#20This is easily the most interesting announcement so far. Machine learning has so many applications, but its use is constrained by the high barriers to entry. Recommendation engines, for example, are huge sales drivers, but few among even the largest ecommerce stores use them. A simple prediction interface that's built on the ML expertise at Google is a win for everyone.
Directed Edge, a promising YC startup, makes recommendation engines surprisingly easy:
It's quite a bit higher-level than what Google is offering here, with all the benefits and drawbacks that entails.