XGBoost is a specific implementation of gradient boosting trees, so
strictly speaking scikit-learn cannot "contain" XGBoost. That being said:
- XGBoost has a scikit-learn compatible API:
https://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn.
So does LightGBM, a
As title
Does sklearn contain xgboost to use?
thanks
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It seems like it's determined by the order in which they occur in the training
set. E.g.,
from sklearn.preprocessing import OneHotEncoder
import numpy as np
x = np.array([['b'],
['a'],
['b']])
ohe = OneHotEncoder()
xt = ohe.fit_transform(x)
xt.todense()
matrix([[0.,
Also Sebastian, I have binary classes but they are strings:
clf.classes_:
array(['American', 'Southwest'], dtype=object)
On Tue, Jan 8, 2019 at 9:51 AM pisymbol wrote:
> If that is the case, what order are the coefficients in then?
>
> -aps
>
> On Tue, Jan 8, 2019 at 12:48 AM Sebastian Rasch
If that is the case, what order are the coefficients in then?
-aps
On Tue, Jan 8, 2019 at 12:48 AM Sebastian Raschka
wrote:
> E.g, if you have a feature with values 'a' , 'b', 'c', then applying the
> one hot encoder will transform this into 3 features.
>
> Best,
> Sebastian
>
> > On Jan 7, 201
Hi,
I'm wondering if anyone is using sklearn-crfsuite on production systems?
Is this library suitable for usage in industry on production systems (and
not academia) for non-big data problems?
Thanks,
Astha
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Apologies I won't be available because of school work.Thanks the whole
community for your great help. I'll continue to contribute and keep online
during the sprint.
Hanmin Qin
- Original Message -
From: Gael Varoquaux
To: Scikit-learn mailing list
Subject: Re: [scikit-learn] Next Sprint
