om/en-us/downloads/03e0ca05-8aa9-49f6-801f-bb23846dc147/
> It implements a much more complicated model, decision tree fields, but can
> also be used for plain random forests.
>
> Cheers,
> Andy
>
>
> On 04/25/2013 03:19 AM, Youssef Barhomi wrote:
>
> Hello,
>
> I am
ready.
On Thu, Apr 25, 2013 at 10:17 AM, Peter Prettenhofer <
peter.prettenho...@gmail.com> wrote:
>
>
>
> 2013/4/25 Youssef Barhomi
>
>>
>> thank you very much Peter,
>>
>> you are right about the n_jobs, something was going wrong with that. W
random_state=1, n_clusters_per_class=1)
>> clf = RandomForestClassifier(max_depth=20, n_estimators=3, criterion =
>> 'entropy', n_jobs = -1, verbose = 10)
>>
>> rng = np.random.RandomState(2)
>> X += 2 * rng.uniform(size=X.shape)
>> linearly_separable
ome
>> to pick it up and update it if you want for your own work, although I'm not
>> sure it would be accepted upstream.
>>
>> I'm sorry I can't be more help - it's tricky trying to replicate work
>> when you have vastly different tools.
>>
>
obviously, would you
recommend an online RF library at this point?
Am 25.04.2013 03:22 schrieb "Youssef Barhomi" :
>
>> Hello,
>>
>> I am trying to reproduce the results of this paper:
>> http://research.microsoft.com/pubs/145347/BodyPartRecognition.pdf wi
_test_split(X, y, test_size=.4)
tic = time.time()
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
print 'Time taken:', time.time() - tic, 'seconds'
--
Youssef Barhomi, MSc, MEng.
Research Software Engineer at the CLPS depart
