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Problem Description “Using Machine Learning to Make Money at Horse Races”

Problem Description “Using Machine Learning to Make Money at Horse Races”. Pos Draw Btn Horse Wgt Jockey Trainer Age SP Comments Raceid

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Problem Description “Using Machine Learning to Make Money at Horse Races”

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  1. Problem Description“Using Machine Learning to Make Money at Horse Races” PosDrawBtnHorseWgtJockeyTrainerAgeSPCommentsRaceid 1 4 Timocracy 10-0 S Drowne A B Haynes 5 4/9 f led after 1f, ridden 2f out, stayed on well and in command final furlong opened 4/5 touched 4/5 £800-£1100 £400-£550 £400-£650 (x3) £400-£750 (x4) £500-£1000 (x4) £300-£600 £200-£400 (x2) 372966 2 5 1¾ Bussell Along (IRE) 9-3 S Sanders Stef Higgins 4 8/1 held up early, headway on outside to chase leaders 4f out, effort and hung left from 2f out, went 2nd 1f out, no chance with winner opened 10/1 touched 10/1 372966 Task: Given a training set; learn a function which predicts the winner/selects a horse to bet $10 on from a given set of entries. Performance Measures: • Accuracy in picking the winner of a race (simple version) • Return of placing a $10 bet on a horse in the race (advanced version; solves the “real problem” trying to make money on the track) Links: http://www.racingpost.com http://www.drf.com/

  2. Problem Description2 • This is an individual project • In general, the problem is a ranking problem; one approach is to learn a function that assigns a score to the horses in a race and pick the horse with the highest score. But it can also be viewed as a classification or prediction problem. • The datasets will be “very basic” only containing a few attributes, but you are allowed to create additional attributes by creating statistics from datasets/by extracting information from other sources (e.g. percentage of races won by a jockey) • Basically, the project tries to predict the future. Likely we will use races of a single race track, given you are true temporal sequence of race: DS1(races in Jan./Feb.), DS2 (races in March/April),…DS6(true testset---you are not allowed to peak into this one; only Chun-sheng has access to this dataset) which serve as training sets, validation sets, test sets, and sources of new feature generation in the project. • Student have freedom in what approaches to use—there are many of them; adhoc approaches are welcome; likely every student will use a different approach, and some will solve the problem. • The goal is to get something running; students who use a well-tuned simple approach will get a better grades than students whouse a very complicated, sophisticated approach which does not run at all. • Deliverables: You will demo your system, write a medium-sized report, and Chun-sheng will test your system with a test set of his own. • You are allowed to use any software/tool in the project; you just have to mention what you used in your report • In general, the submission deadline is We., March 23, 11p, but the idea is you spent at most 5 weeks on the project!

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