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
