Hi,
I guess the error was due to the fact that I was using One hot encoding of
a data frame which include Strings.
Currently, I started using Dict Vectorizer to encode both my categorical
variables ( in integers) and categorical variables which are strings.
It seems to be working fine.
My Y is as follows
import scipy.sparse as sps
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer()
# Convert Panda Data frame to Dict
train_df = df_modified[['locationid','dep_departtime',
'arr_arrivetime',
'arr_departtime',
'dep_arrivetime',
'departureairport_or_point',
'destinationairport_or_point',
'bookeddate',
'departuredate']]
train_dict = train_df.T.to_dict().values()
# Create Fit_Transform
b=vec.fit(train_dict)
a=b.transform(train_dict)
I hope I am working in the right direction. Let me know your thoughts
Regards,
Sanant
Subject: Re: [Scikit-learn-general] Multi Label classification using
OneVsRest Classifier
To: scikit-learn-general@lists.sourceforge.net
Message-ID: <565e7223.3090...@gmail.com>
Content-Type: text/plain; charset="windows-1252"
Please provide the full traceback.
What is the type of y here, and what are its entries?
On 11/30/2015 07:45 PM, Startup Hire wrote:
> Hi Pypers,
>
> Hope you are doing well.
>
> I am doing multi label classification in which my X and Y are sparse
> matrices with Y properly binarized.
>
> Though my Y has multi-labels properly binarized, I am getting the
> following error:
>
> Value Error: Multioutput target data is not supported with label
binarization
>
>
> The Classifier I am using is as follows:
>
> Classifier =
> OneVsRestClassifier(SGDClassifier(random_state=0,loss='log',alpha=0.00001,
penalty='elasticnet')).fit(Finaldata,y)
>
> Let me know in what are the ways this can be resolved. Should I make
> any upstream changes?
> Regards,
> Sanant
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