On Mon, Sep 24, 2012 at 06:34:36PM +0200, Sheila the angel wrote:
> 1. I am trying to understand how exactly this probability is calculated. The
> document says "probability model is created using cross validation"
Using Platt scaling that is calibrated by cross-validation.
> So I think to calcul
Hi Ariel,
On Tue, Sep 25, 2012 at 05:44:21PM -0700, Ariel Rokem wrote:
> Initially, I suspected that this has to do with the non-negativity
> constraint I applied, so I removed that.
Indeed, if you are imposing positivity, you do not have a least square.
> Then, I was wondering whether it might
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`
Hi everyone,
I am still trying to understand ElasticNet. Here's my description (from a
previous thread) of the kind of problem I am trying to solve:
On Mon, Sep 17, 2012 at 9:56 AM, Ariel Rokem wrote:
> I am using the sklearn.linear_model.ElasticNet class to fit some data. The
> structure of th
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
2012/9/24 Ark :
> Olivier Grisel writes:
>
>> You can use the Pipeline class to build a compound classifier that
>> binds a text feature extractor with a classifier to get a text
>> document classifier in the end.
>>
> Done!
>
>>
>> 7s is very long. How long is your text document in bytes ?
> The
2012/9/24 Joseph Turian :
> Chris Lin iirc has advocated partitioning the examples then concatenation the
> individual classifiers.
>
> You could do that and then do a second pass of learning: find the 1% of
> examples that are the hardest for the ensemble and learn over them.
>
> Regardless, it
Chris Lin iirc has advocated partitioning the examples then concatenation the
individual classifiers.
You could do that and then do a second pass of learning: find the 1% of
examples that are the hardest for the ensemble and learn over them.
Regardless, it will be adhoc unless you use an out of
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