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Ask HN: What are you working on (hacking)?

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Re: Ask HN: What are you working on (hacking)?

#22

Post by vosper »

user24 wrote: post #14
vosper wrote: post #11

My ongoing quest to make money predicting horse races. So far I've not made a dime, and don't really expect to, but the faint hope of future monetary rewards keeps me going. The real payoff has been that in the process I've learned all sorts of things: about Python, data mining, working with large datasets, machine learning...

I've been thinking of trying to write a machine learning app to do just that. Any hints?

I didn't implement the machine learning algorithms for myself, because there are some really good packages out there and I know I don't have the smarts to better them.

Keep in mind that I didn't really have any success:

There seem to be two main ML packages, Weka and Orange. I personally preferred Orange, it has a nice graph-based UI for linking various components together; when you've figured that out it can script in Python. Also Orange makes it easy to test your data set against various different learning systems, and compare the performance. Standard testing procedures like n-fold cross-validation are built-in and really simple to use.

Also you need data. I'm pretty sure more is always better. I actually started with greyhounds* and skimmed mine (in Python use BeautifulSoup) from a website. I tried to come up with various statistics about the recent performance of the dogs. Unfortunately nothing I tried made the ML algorithms predict better than a random choice. A friend who's into gambling suggested greyhound racing was quite random by nature, so I've switched to horses recently. I'm still building that dataset, now trying out MongoDB just for fun.

I think the trouble is that you can have as much raw data as you like, but generating the predictive statistics requires a lot of knowledge of the problem domain. I'm not actually into gambling at all so I don't know if the track conditions are important, how much breeding or the age of the animal really matters etc... This made it hard to pick likely stats (and rebuilding datasets and retraining learners can take some time).

For horses there's a lot more information in forums and racing guides etc, so I'd start with horses. Just make sure you've tested your predictions with pretend bets before you commit any real money :)

Good luck!

*I began with greyhounds because of a dissertation posted on reddit where the authors suggested they'd had some success with a neural network and gave quite a lot of detail. That piqued my curiosity, and my initial version just re-implemented their work.

EDITED: For clarity

Re: Ask HN: What are you working on (hacking)?

#23

Post by Vivtek »

http://search.cpan.org/~michael/Class-Declarative-0.06/lib/C...

A declarative framework for Perl that lets you set up complex data structures and code in an easy-to-read format.

A complete working example GUI program, using a not-yet-published set of semantic classes:

  use Class::Declarative qw(Wx::Declarative);

  dialog (xsize=250, ysize=110) "Wx::Declarative dialog sample"
    field celsius (size=100, x=20, y=20) "0"
    button celsius (x=130, y=20) "Celsius" { $^fahrenheit = ($^celsius / 100.0) * 180 + 32; }
    field fahrenheit (size=100, x=20, y=50) "32"
    button fahrenheit (x=130, y=50) "Fahrenheit" { $^celsius = (($^fahrenheit - 32) / 180.0) * 100; }

Re: Ask HN: What are you working on (hacking)?

#24

Post by njl »

My Django hosting service. I looked at Heroku and thought, "I want that for Python." Yesterday, I got most of the http request path finished. There is some node.js work left, and I still need to do some more work on Varnish. I'm hoping to get to the Postgresql stuff next week, then on to the website and API...

Re: Ask HN: What are you working on (hacking)?

#25

Post by user24 »

vosper wrote: post #22
user24 wrote: post #14

Earlier quoted context omitted.

I've been thinking of trying to write a machine learning app to do just that. Any hints?

I didn't implement the machine learning algorithms for myself, because there are some really good packages out there and I know I don't have the smarts to better them. Keep in mind that I didn't really have any success: There seem to be two main ML packages, Weka and Orange. I personally preferred Orange, it has a nice graph-based UI for linking various components together; when you've figured that out it can script…

Thankyou for the detailed reply.

Yeah I hated using weka at uni. I'll look into Orange.

"I don't know if the track conditions are important, how much breeding or the age of the animal really matters etc."

Yeah, feature selection is a tough one. I'd thought that the system would pick up on good indicators by itself, but it might well be that that has to be a manual decision.

"Just make sure you've tested your predictions with pretend bets before you commit any real money"

haha, yeah absolutely. My plan was to train/test until the accuracy seemed good enough (using monte carlo) and then run the system on live data with pretend money for a few months to see what the actual performance is like, before actually investing real cash.

Do you have a link to the greyhound topic? I searched on google but couldn't find it.

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