Thanks millions to Paolo, Gael and everybody.
Adnan
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2011/12/30 Gilles Louppe :
>> It seems to be an interesting tool to me. We need to find a
>> non-trivial overfitting example that would run in an acceptable time
>> with the datasets available in the scikit.
>
> Actually, those curves can be plot with respect to any parameter, not
> only the traini
> It seems to be an interesting tool to me. We need to find a
> non-trivial overfitting example that would run in an acceptable time
> with the datasets available in the scikit.
Actually, those curves can be plot with respect to any parameter, not
only the training set size.
What comes to me is t
2011/12/28 Nick Wilson :
> On Tue, Dec 27, 2011 at 6:23 PM, Olivier Grisel
> wrote:
>> Hi all,
>>
>> I came across the following blog post about Andrew Ng's ML class and I
>> like the training / validation errors plots to find out whether the
>> model is too biased (underfitting) or two lax (high
Hi Gael,
On Thu, Dec 29, 2011 at 11:06 PM, Gael Varoquaux <
[email protected]> wrote:
>
> To the other developers: is their a reason/difficulty for not having
> Platt's method (implemented for SVC, AFAIK) for LinearSVC?
>
I've got a "draft" Platt's calibration implementation on a bran
Hi list,
This is a call to get an additional person (or more) to review the
pending PR #491 on parallel forest of trees.
It has already been reviewed by @ogrisel and look ready to merged for
the both of us, but an additional review would be more than welcome!
https://github.com/scikit-learn/scik
In the previous mail variable `X` should be replaced by `data`.
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Olivier
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2011/12/30 Bronco Zaurus :
> Thank you for all the answers. Yes, I'm not dealing with arbitrary strings,
> just a set of possible values, so the binary representation seems OK.
Alright, then the name of this kind of features is "categorical
features" in machine learning jargon: the string is used
Thank you for all the answers. Yes, I'm not dealing with arbitrary strings,
just a set of possible values, so the binary representation seems OK.
One more way would be computing classification probability for each value
and plugging the resulting number back into data. For example, let's say
there
On Fri, Dec 30, 2011 at 11:28:39AM +0100, Andreas Mueller wrote:
> It might be that I haven't really understood the meaning of ROC
> curves, but I thought it worked like @ogrisel said.
> Whatever the correct method to produce a ROC curve
> from a linear classifier, I'm pretty sure that using the de
On 12/30/2011 10:15 AM, Gael Varoquaux wrote:
> On Fri, Dec 30, 2011 at 10:09:59AM +0100, Olivier Grisel wrote:
>>> * You could use the decision function, (decision_function method of the
>>>LinearSVC) although this is clearly a hack.
>> Why is this a hack? ROC is only concerned with the rela
On Fri, Dec 30, 2011 at 10:09:59AM +0100, Olivier Grisel wrote:
> > * You could use the decision function, (decision_function method of the
> > LinearSVC) although this is clearly a hack.
> Why is this a hack? ROC is only concerned with the relative positions
> of the decision threshold, not th
2011/12/29 Gael Varoquaux :
> On Thu, Dec 29, 2011 at 12:46:36PM -0800, adnan rajper wrote:
>> I use LinearSVC for text classification. My problem is that I want to
>> generate ROC curve for LinearSVC. Since LinearSVC does not output
>> probabilties. Is there any other way to generate ROC
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