> I'm basically looking to take pre-trained classifiers and allows you
> to combine the predicted probabilities in custom ways, like favoring
> some classifiers over others, etc.
>
> Not that RandomForests™ are not useful--they could be the building
> block classifiers in such a system.
>
> @Oliver
On 09/25/2012 11:19 PM, Olivier Grisel wrote:
> I think we could have `classes=None` constructor parameter in
> SGDClassifier an possibly many other classifiers. When provided we
> would not use the traditional `self.classes_ = np.unique(y)` idiom
> already implemented in some classifiers of the pr
@Gilles,
Thanks for the link. Those classes basically implement a paper that
has a specific idea of RandomForests™ (no kidding, it's trademarked),
with bootstrapping, oob estimation, and n trees trained on the same
data.
I'm basically looking to take pre-trained classifiers and allows you
to comb
@Doug: Sorry I was typing my previous response from my phone.
The snippet of code that I was talking about can be found at:
https://github.com/glouppe/scikit-learn/blob/master/sklearn/ensemble/forest.py#L93
Cheers,
Gilles
On Wednesday, 26 September 2012, Gilles Louppe wrote:
> Hi,
>
> The ense
Hi,
The ensemble classes handle the problem you describe already. Have a look
at the implementation of predict_proba of BaseForestClassifier in
ensemble.py if you want to do that yourself by hand.
Hope this helps.
Gilles
On Wednesday, 26 September 2012, Mathieu Blondel
wrote:
>
>
> On Wed, Sep
On Wed, Sep 26, 2012 at 3:52 AM, Doug Coleman wrote:
>
>
> If you examine the code, fit() "warms up" the optimization with some
> additional parameters, then calls _partial_fit(). partial_fit() just
> calls _partial_fit() directly. So, it looks like fit() and
> partial_fit() could take a `classes`
I think we could have `classes=None` constructor parameter in
SGDClassifier an possibly many other classifiers. When provided we
would not use the traditional `self.classes_ = np.unique(y)` idiom
already implemented in some classifiers of the project (but not all).
+1 also for raising a ValueError
I'm not necessarily looking for a quick fix here, and anything I would
consider trying to contribute to scikit would be useful and correct.
You're right that there's not a good chance it can learn to predict
with sparse output classes, but if the problem were easy, then I
wouldn't need scikit at a
On Tue, Sep 25, 2012 at 10:31:10AM -0700, Doug Coleman wrote:
> I'm making an ensemble of trees by hand for classification and trying
> to merge their outputs with predict_proba. My labels are integers
> -2..2. The problem is that -2 and 2 are rare labels. Now assume that I
> train these trees with
I'd love to submit a patch.
Looking at SGDClassifier docs, the __init__ doesn't take a classes
parameter, but instead there's a partial_fit() that takes `classes`
exactly like I'd except. However, the docs for partial_fit() are
exactly the same as for fit().
If you examine the code, fit() "warms
2012/9/25 Doug Coleman :
> label. So to merge predictions from the trees, now I have to do
> bookkeeping to remember which trees had which labels in them, and it's
> a mess.
You did discover the classes_ attribute, did you? That keeps track of
the classes found in y by fit and solves part of the b
Hi,
I'm making an ensemble of trees by hand for classification and trying
to merge their outputs with predict_proba. My labels are integers
-2..2. The problem is that -2 and 2 are rare labels. Now assume that I
train these trees with different but related data sets, some of which
don't even contai
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