ps. a quick update, the notebook now matches the code in the branch.
On Tue, Sep 16, 2014 at 9:54 PM, wrote:
> satra,
>
> thanks so much for pointing me to this. much appreciated!
>
> best,
> kc
>
> > hi kc,
> >
> > it's not in scikit learn but we use these quite routinely alongside
> > scikit-l
satra,
thanks so much for pointing me to this. much appreciated!
best,
kc
> hi kc,
>
> it's not in scikit learn but we use these quite routinely alongside
> scikit-learn.
>
> https://github.com/scikit-learn/scikit-learn/pull/2730
>
> here is also a set a notebook showing manifold extraction usin
hi kc,
it's not in scikit learn but we use these quite routinely alongside
scikit-learn.
https://github.com/scikit-learn/scikit-learn/pull/2730
here is also a set a notebook showing manifold extraction using diffusion
embedding. the notebook is a little out of date with respect to the code.
it a
Has anyone worked on the problem of manifold alignment?
http://en.wikipedia.org/wiki/Manifold_alignment
as described in papers like:
"Manifold Alignment without Correspondence"
http://ijcai.org/papers09/Papers/IJCAI09-214.pdf
or
"Data Fusion and Multi-Cue Data Matching by Diffusion Maps"
http:
Sounds like an interesting idea. It could be done by editing a new
wiki page on github:
https://github.com/scikit-learn/scikit-learn/wiki
--
Olivier
--
Want excitement?
Manually upgrade your production database.
When yo
yes - In fact my real goal is to implement RGF ultimately, though I had
considered building/forking off the current GradientBoostingRegressor
package as a starting point A) b/c I'm new to developing for scikit-learn
and B) to maintain as much consistency as possible with the rest of the
package.
U
Of you set the random state and put the same parameters, you are expected to
have
exactly the same model. To be concrete, if you do
est_1 = GradientBoostingClassifie(random_state=0)
est.fit(X, y)
est_2 = GradientBoostingClassifie(random_state=0)
est.fit(X, y)
est_3 = GradientBoostingClassifie(r
Hi all,
Wanted to see if anyone had any resources for best practices in setting up
config files in a machine learning context. I am working on an ensemble
classifier and trying to figure out the best way to organize
training/saving/loading feature-level classifiers and the ensemble
classifier via
The only reference I know is the Regularized Greedy Forest paper by Johnson
and Zhang [1]
I havent read the primary source (by Zhang as well).
[1] http://arxiv.org/abs/1109.0887
2014-09-16 15:15 GMT+02:00 Mathieu Blondel :
> Could you give a reference for gradient boosting with fully corrective
Thanks Arnaud
Got it.
Essentially what you are saying is
While training classifier A, imagine there was a tie at estimator 3, on 2
features sets, e.g S1[12,3,4,5,6] and S2[2,3,4,5,6,7]. And S1 was chosen
While training classifier B, there was a tie again at estimator 3 on the
same sets and S2 was
Could you give a reference for gradient boosting with fully corrective
updates?
Since the philosophy of gradient boosting is to fit each tree against the
residuals (or negative gradient) so far, I am wondering how such fully
corrective update would work...
Mathieu
On Tue, Sep 16, 2014 at 9:16 AM
And after many years of using them both I still get the two confused...
Sorry about the noise! ;)
On Tue, Sep 16, 2014 at 12:47 PM, Gael Varoquaux <
[email protected]> wrote:
> On Tue, Sep 16, 2014 at 12:43:49PM +0200, Anders Aagaard wrote:
> > I just had a look at this, and the docum
During the growth of the decision tree, the best split is searched in a subset
of max_features sampled among all features.
Setting the random_state allows to draw the same subsets of features each time.
Note that if several candidate splits have the same score, ties are broken
randomly. Setting t
Agree Gilles
Which is why I later changed to max_features = None, but 6 is a good value,
sqrt(36) ~=sqrt(30) and we had 30 features.
Generally speaking, if I have 100 estimators (this is from previous
experience and also the auto setting on your GBC) and 30 features, 6 should
be a good start.
But
Hi Deb,
In your case, randomness comes from the max_features=6 setting, which
makes the model not very stable from one execution to another, since
the original dataset includes about 5x more input variables.
Gilles
On 16 September 2014 12:40, Debanjan Bhattacharyya wrote:
> Thanks Arnaud
>
> ra
On Tue, Sep 16, 2014 at 12:43:49PM +0200, Anders Aagaard wrote:
> I just had a look at this, and the documentation on http://scikit-learn.org/
> stable/modules/generated/sklearn.linear_model.LogisticRegression.html states y
> should be "y : array-like, shape = [n_samples]",
That's a logistic regre
I just had a look at this, and the documentation on
http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
states y should be "y : array-like, shape = [n_samples]", did I miss
something? I also tried doing it real quick, and it immediately complained
on the in
Thanks Arnaud
random_state is not listed as a parameter on
http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
page.
But it is listed as an argument in the constructor. Its my fault probably -
that I did not notice it as a passable parameter. May be th
Hi,
To get reproducible model, you have to set the random_state.
Best regards,
Arnaud
On 16 Sep 2014, at 12:08, Debanjan Bhattacharyya wrote:
> Hi I recently participated in the Atlas (Higgs Boson Machine Learning
> Challenge)
>
> One of the models I tried was GradientBoostingClassifier. I
Hi I recently participated in the Atlas (Higgs Boson Machine Learning
Challenge)
One of the models I tried was GradientBoostingClassifier. I found it
extremely non deterministic.
So if I use
est = GradientBoostingClassifier(n_estimators=100,
max_depth=10,min_samples_leaf=20,max_features=6,verbose
Hi,
There is a very advanced pull request which add sparse matrix support to
decision tree: https://github.com/scikit-learn/scikit-learn/pull/3173
Based on this, it could be possible to have gradient tree boosting working
on sparse data. Note that adaboost already support sparse matrix
with non-
I would add to this lists:
- check_array;
- check_consistent_length;
- check_X_y.
Those are very useful.
Arnaud
On 15 Sep 2014, at 20:03, Olivier Grisel wrote:
> 2014-09-15 6:40 GMT-07:00 Mathieu Blondel :
>> lightning is using the following utils:
>>
>> - check_random_st
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