Good morning all,
I am making some machine learning on the metro traffic volume from there: http://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volume 4
The aim is to predict an hourly traffic volume according to various features that are:

holiday : Yes/No
temperature in C
rain and snow fall in mm
clouds cover %
hours of the day : integer from 0 to 23
day of the week (wd_i, i=0…6).

I made a random forest and get a 20% error on traffic prediction, which is OK for me. Then I have plotted the FEATURES IMPORTANCE and got:
Feature ranking:

holiday  (0.736229)
temp  (0.137736)
rain_1h  (0.029498)
snow_1h  (0.028497)
clouds_all  (0.023778)
weather_main (0.019016)
hours  (0.004792)
wd_0  (0.004365)
…
wd_6 0 (0.000018)

Comments :

‘holiday’ is very important because of course, on holidays, traffic is very slow the whole day !
‘hours’ seems to be almost insignificant.

Action: I have REMOVED  ‘hours’ from the features because it was so insignificant and then I run random forest again.
Result: result is a disaster (error jumped from 20 to 60%) and YES, it is normal, because traffic volume depends so much on the hour of the day !
Question:

why was ‘hours’ put with a so low ranking on the features importance analysis ?
it seems not being a good  idea at all to withdrawn features based on there .feature_importances_ method of RandomForestRegressor(). Is that correct ?
So what does .feature_importances_ precisely measure ?

Thanks in advance for your comments !