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Google Prediction API

code.google.com

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Re: Google Prediction API

#21

Post by snippyhollow »

conesus wrote: post #7
mseebach wrote: post #4

Are 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…

You scratched the itch I procrastinated to scratch. Others (à la Postrank) tried, you just seem you have tried better! 1 Kudo :) EDIT: Ah, you're the guy behind Raphaël Vector lib, have one more Kudo!

Re: Google Prediction API

#24

Post by physcab »

m0th87 wrote: post #10

This 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.

For very specific problems, yes. The translation classification example is definitely a type of prediction problem that would be good for this service.

However, this is far from a silver bullet to ML problems. It can be quite dangerous, for example, to send off a bunch of data to google and immediately trust their analysis without knowing the underlying application of their algorithm. As a researcher in this area, what I would LOVE is if I could create my own algorithm, send it to Google, upload enormous amounts of data, and get back a result. Because right now it's difficult to scale complex algorithms to datasets in the GB-TB-PB range. Mahout is taking a valid stab at this problem though.

Re: Google Prediction API

#25

Post by [deleted] »

sketerpot wrote: post #14
conesus wrote: post #7

Earlier quoted context omitted.

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. http://www.cs.waikato.ac.nz/ml/weka/

[deleted]

Re: Google Prediction API

#26

Post by conesus »

snippyhollow wrote: post #21
conesus wrote: post #7

Earlier quoted context omitted.

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…

You scratched the itch I procrastinated to scratch. Others (à la Postrank) tried, you just seem you have tried better! 1 Kudo :) EDIT: Ah, you're the guy behind Raphaël Vector lib, have one more Kudo!

Raphaël was written by Dmitry Baranovskiy. I only made very minor contributions to the library (animation easing, which are pretty ubiquitous in the examples now) and presented the library at the SVG Open 2009 at Google.

I made Protopub to scratch the itch I think a LOT of us have. I am about a month away from a v1.0, and that's when I'll announce it on HN. Until then, I'm tweaking AI algorithms, fixing UI bugs, and making sure the back-end can handle the more than moderate traffic that HN will send. The few users I get from posts like this are enough to do some basic testing.

Re: Google Prediction API

#27

Post by jim-greer »

sketerpot wrote: post #14
conesus wrote: post #7

Earlier quoted context omitted.

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. http://www.cs.waikato.ac.nz/ml/weka/

I recently used Weka to create a simple rules-based fraud model for Kongregate. It worked very well, and had a lot of options for algorithms. The UI is a little weird, but it's worth checking out.

Re: Google Prediction API

#28

Post by carbocation »

jey wrote: post #18
carbocation wrote: post #16

From 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.

According to what they wrote, there is no separate testing set, so they estimate accuracy based on the training set. They use cross-validation to reduce their overfitting bias. (I could be wrong since I haven't actually used their service, but this is the take-home message of the wording of their explanation. If they intended to convey otherwise, they used the wrong language.)

Re: Google Prediction API

#29

Post by haidut »

conesus wrote: post #7
mseebach wrote: post #4

Are 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…

Can you please try our news recommendation service and let me know how it stacks up against your site in terms of (perceived) accuracy? The site is http://www.euraeka.com I can provide more info if you need it. Email me haidut (at) gmail (dot) com. I just need some feedback from someone who has built something similar/related.

Re: Google Prediction API

#30

Post by izendejas »

jbrennan wrote: post #2

I knew this was coming.

I had this idea myself, basically machine learning as a service about 3 years or more ago. Somehow, I also knew Google would implement something like this. So while I still consider this a viable startup idea, I knew it would be tough to compete against a behemoth that already has tons of data and experience training countless machine learning algorithms.
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