I am trying to use NMF from scikit learn. Given a matrix A this should
give me a factorization into matrices W and H so that WH is
approximately equal to A. As a sanity check I tried the following:
from sklearn.decomposition import NMF
import numpy as np
A = np.array([[0,1,0],[1,0,1],[1,1,0]])
nmf
Hi Raphael,
The other matrix in the factorization is the output of nmf.transform(A).
In your example you forgot to fit the estimator; if you're just
interested in the decomposition the recommended way is to get it in
one line with W = nmf.fit_transform(A).
While the mathematical description doesn
Hi all,
I am currently tuning some parameters of my xgboost model using scikit's
grid_search, e.g.:
param_test1 = {'max_depth':range(3,10,2),
'min_child_weight':range(1,6,2)
}
gsearch1 = GridSearchCV(estimator = XGBClassifier(learning_rate =0.1,
n_estimators=762,
You can use a pipeline object to contain both feature
selection/transformation steps and an estimator. All elements of a pipeline
can then be tuned using gridsearch. You can see a simple example here:
http://scikit-learn.org/stable/modules/pipeline.html
You may also be interested seeing if the Fea
Hi, Piotr,
> These preprocessing steps have some parameters too, which I would like to
> tune.
> I know that it is possible to tune the parameters of the preprocessing steps,
> if they are part pf my pipeline.
> E.g. if I am using PCA, I could tune the parameter n_components, right?
>
> But w
Hi, all,
I noticed that it takes forever now until something is posted on the mailing
list after I sent it out. Since the switch to Python.org, it takes about ~15 -
45min after hitting “sent”. I’ve noticed this for months now and was wondering
if this is normal or of there’s something going on w
Hi Sebastian,
thanks a lot. That was exactly what I was looking for! :)
I will have a look into the base classes of other preprocessing steps as
well.
@Jacob
Thank you too! :)
Greets,
Piotr
On 07.09.2016 20:26, Sebastian Raschka wrote:
> Hi, Piotr,
>
>
>> These preprocessing steps have some p
