The problem is really how you do cross-validation.
On 02/09/2016 11:47 PM, muhammad waseem wrote:
Thanks Luca and Andreas, the idea behind this is to predict a weather
parameter using some other parameters. You still think it will be
difficult to solve with Random Forest as it is not really time series.
I get good training results (with high max_depth) but not very good
for the testing dataset, meaning the regressor is unable to generalise.
What about gradient boosting regressor, is this suitable?
Thanks
Kindest Regards
Waseem
On Tue, Feb 9, 2016 at 10:00 PM, Luca Puggini <lucapug...@gmail.com
<mailto:lucapug...@gmail.com>> wrote:
Personally I think that random forest should not be used for time
series data unless the data is supposed to have some sort of
periodicity. This is because random forest is a sort of local
estimator. It's not effective if new samples are outside of the
hypercube defined by the training data. This is quite common in
time series. If I were you I would try something like linear
regression or extreme learning machine. If you are interested in
extreme learning machine there should be a PR on scikit-learn (I
wrote a simple paper with a simple introduction to ELM: "Extreme
learning machines for virtual metrology and etch rate prediction".
Maybe this can help you
.
On Tue, Feb 9, 2016, 9:41 PM Andreas Mueller <t3k...@gmail.com
<mailto:t3k...@gmail.com>> wrote:
Yes. Exactly what Luca said and what I said earlier.
There is temporal structure in your data. If you use k-fold
cross validation (or even shuffle the data) that destroys the
temporal structure.
You want to make predictions for the future (the second file).
You should use a cross-validation method that tries to predict
form the past
to the future, not that tries to predict arbitrary time
points. Otherwise, your results will be too optimistic, as you
found.
On 02/09/2016 04:23 PM, muhammad waseem wrote:
I have it in separate file (csv). Actually, I have four years
weather data (hourly values in two files), I use 3 years
(first file) worth of data for training and one years worth
of data (second file) for testing.
Am I doing it correctly? any ideas?
On Tue, Feb 9, 2016 at 9:01 PM, Andreas Mueller
<t3k...@gmail.com <mailto:t3k...@gmail.com>> wrote:
How did you create the hold-out test data? Before or
after shuffling?
On 02/09/2016 03:22 PM, muhammad waseem wrote:
Hi Andreas,
Thanks for your reply. I have already shuffled my data
so it is not in ordered now but still no luck. Any other
suggestions?
On Tue, Feb 9, 2016 at 8:16 PM, Andreas Mueller
<t3k...@gmail.com <mailto:t3k...@gmail.com>> wrote:
You should probably use a different cross-validation
strategy if your
data is ordered. This will give you more realistic
cross-validation results.
There was a time series CV object somewhere, and by
now I think we
should include it (this is the third time this comes
up in the last 3 days)
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