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On 1/10/13 11:14 AM, Walshe, Maurice (RBI-UK) wrote:
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-----Original Message-----
From: akshay shetye [mailto:[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
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