Yes, according to the code it does not support sample_weight but just
class_weight.
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Olivier
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That is for class weights, not sample weights, right?
On 04/21/2015 04:03 AM, Olivier Grisel wrote:
> The docstring is not accurate: it does not resample, but instead
> reweight C for each class:
>
> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/svm/src/liblinear/linear.cpp#L239
The docstring is not accurate: it does not resample, but instead
reweight C for each class:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/svm/src/liblinear/linear.cpp#L2395
Feel free to send an PR to fix the docstring.
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Olivier
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@Mathieu
http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances
So, if class weights are available why sub/oversample?
2015-04-21 3:34 GMT+02:00 Mathieu Blondel :
> Last time I checked, liblinear didn't support sample weights, just class
> weights (one for positive samples and
Last time I checked, liblinear didn't support sample weights, just class
weights (one for positive samples and another for negative samples).
Mathieu
On Tue, Apr 21, 2015 at 5:56 AM, iBayer wrote:
> Hi,
> I was surprised to read that class weights are implemented via sampling
> for LogisticReg
Hi,
I was surprised to read that class weights are implemented via sampling
for LogisticRegression, is this really the case?
from the LR doc
---
class_weight : {dict, 'auto'}, optional
Over-/undersamples the samples of each class according to the given
weights. If not given, a
