Who is the moderator for our lists? On Thu, Jan 10, 2013 at 9:04 AM, Jeff Eastman <[email protected]>wrote:
> To unsubscribe from this list, send an email to user-unsubscribe@mahout.** > apache.org <[email protected]> > > > On 1/10/13 11:14 AM, Walshe, Maurice (RBI-UK) wrote: > >> unsubscribe >> >> -----Original Message----- >> From: akshay shetye >> [mailto:akshay.shetye@gmail.**com<[email protected]> >> ] >> Sent: 10 January 2013 14:40 >> To: [email protected] >> Subject: Re: machine learning algorithm giving wrong results >> >> Thanks for replying.This is not a homework problem ,I just related my >> real use case to a simple example problem. >> >> I did regression and problem i was facing is i get negative predictions >> of travel time. >> There are many other features in input feature list , Day of week : (on >> some days he goes to church /temple on the way) Start time : (there will >> be more traffice at 8.30 than 7) etc >> >> Can u elaborate more and tell about the solution.Am not a expert in >> machine learning . am new to this. >> >> Note :sorry for starting new thread i somehow dont get mails of this >> user group in this mail Regards, Damodar >> >> -- This is a regression problem. The regression algorithm available in >> Mahout >> >> is logistic regression. You can force it to solve this problem in two >> ways. First, you can scale and offset the output by a large enough >> factor so that the normal 0 to 1 output range is much larger than >> necessary and the mean is centered at the rough mean of your data. The >> only input feature would be wake-up time. >> >> Another approach would be to use multinomial output with three outputs. >> This is a more natural fit to the Mahout algorithm. >> >> Is this a homework problem? >> >> On Wed, Jan 9, 2013 at 9:16 PM, akshay shetye >> <[email protected]>**wrote: >> >> I have a machine learning problem which i am illustrating by giving a >>> simile ,less complex example >>> >>> John goes from home to office daily.He takes following time to reach >>> to office >>> >>> Bus -> 3 hours >>> Cab -> 2 hours >>> bike -> 1 hours >>> >>> Problem:How much time john will take to reach his office from the time >>> he starts. >>> >>> He mostly takes bus and sometimes cab and rarely bike depending on how >>> much time he has to reach his office >>> >>> He must reach at office at 9am. >>> >>> Now if he starts at 6 he takes bus >>> if he starts at 7 he takes cab >>> if he starts at 8 he takes bike. >>> >>> Now the model which i build using M5P and libSvm predicts fine when he >>> starts on or before 8.Now the problem occurs when John leaves his home >>> after 8 (eg 8.30 or 9 /assume he got up late) . Ideally in this case >>> he will take around 1 hour as he should take his bike. >>> >>> My model is giving me negative predictions and this is what is causing >>> problem. >>> >>> Now as john wakes up late very rarely we have very few data points to >>> train it on such cases. >>> >>> My feature list is as follows >>> >>> timeLeftForDuty, DAY_OF_WEEK , TRAVEL_TIME >>> >>> TRAVEL_TIME is we are trying to predict. >>> >>> How can solve this problem?Meaning how can i avoid getting negati >>> values of travel time?Which algorithm should i use from mahout? >>> >>> -- >>> Regards, >>> Damodar Shetyo >>> >>> >> Regards, >> Damodar Shetyo >> >> ===================== DISCLAIMER ====================== >> This message is intended only for the use of the person(s) >> ("Intended Recipient") to whom it is addressed. It may contain >> information which is privileged and confidential. Accordingly >> any dissemination, distribution, copying or other use of this >> message or any of its content by any person other than the Intended >> Recipient may constitute a breach of civil or criminal law and is >> strictly prohibited. If you are not the Intended Recipient, please >> contact the sender as soon as possible. >> >> Reed Business Information Limited. >> Registered Office: Quadrant House, The Quadrant, Sutton, Surrey, SM2 5AS, >> UK. >> Registered in England under Company No. 151537 >> >> ==============================**========================= >> >> >> >
