Can we avoid using one hot encoding on a categorical data which has only 2 unique values?
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I am using an MLP Classifier. There are around 4-5 categorical attributes (such as Gender(Male/Female), Smoking(Yes/No), Diabetes(Yes/No), Hypertension(Yes/No)).
Do I necessarily need to use one hot encoding on all these features before using a neural network classifier? I don't have a lot of training data (only 130 samples).
Can I just get away with Label Encoding these attributes?
python machine-learning scikit-learn
edited Nov 17 '18 at 0:11
desertnaut
20.6k84379
asked Nov 16 '18 at 13:58
Gautam PhadkeGautam Phadke
1
add a comment |
I am using an MLP Classifier. There are around 4-5 categorical attributes (such as Gender(Male/Female), Smoking(Yes/No), Diabetes(Yes/No), Hypertension(Yes/No)).
Do I necessarily need to use one hot encoding on all these features before using a neural network classifier? I don't have a lot of training data (only 130 samples).
Can I just get away with Label Encoding these attributes?
python machine-learning scikit-learn
edited Nov 17 '18 at 0:11
desertnaut
20.6k84379
asked Nov 16 '18 at 13:58
Gautam PhadkeGautam Phadke
1
Read the accepted answer here
– AkshayNevrekar
Nov 16 '18 at 14:01
add a comment |
I am using an MLP Classifier. There are around 4-5 categorical attributes (such as Gender(Male/Female), Smoking(Yes/No), Diabetes(Yes/No), Hypertension(Yes/No)).
Do I necessarily need to use one hot encoding on all these features before using a neural network classifier? I don't have a lot of training data (only 130 samples).
Can I just get away with Label Encoding these attributes?
python machine-learning scikit-learn
edited Nov 17 '18 at 0:11
desertnaut
20.6k84379
asked Nov 16 '18 at 13:58
Gautam PhadkeGautam Phadke
1
I am using an MLP Classifier. There are around 4-5 categorical attributes (such as Gender(Male/Female), Smoking(Yes/No), Diabetes(Yes/No), Hypertension(Yes/No)).
Do I necessarily need to use one hot encoding on all these features before using a neural network classifier? I don't have a lot of training data (only 130 samples).
Can I just get away with Label Encoding these attributes?
python machine-learning scikit-learn
python machine-learning scikit-learn
edited Nov 17 '18 at 0:11
desertnaut
20.6k84379
asked Nov 16 '18 at 13:58
Gautam PhadkeGautam Phadke
1
edited Nov 17 '18 at 0:11
desertnaut
20.6k84379
asked Nov 16 '18 at 13:58
Gautam PhadkeGautam Phadke
1
edited Nov 17 '18 at 0:11
desertnaut
20.6k84379
edited Nov 17 '18 at 0:11
desertnaut
20.6k84379
edited Nov 17 '18 at 0:11
desertnaut
20.6k84379
20.6k84379
asked Nov 16 '18 at 13:58
Gautam PhadkeGautam Phadke
1
asked Nov 16 '18 at 13:58
Gautam PhadkeGautam Phadke
1
asked Nov 16 '18 at 13:58
Gautam PhadkeGautam Phadke
1
1
Read the accepted answer here
– AkshayNevrekar
Nov 16 '18 at 14:01
add a comment |
Read the accepted answer here
– AkshayNevrekar
Nov 16 '18 at 14:01
Read the accepted answer here
– AkshayNevrekar
Nov 16 '18 at 14:01
Read the accepted answer here
– AkshayNevrekar
Nov 16 '18 at 14:01
add a comment |
1 Answer
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Of course, that will be enough. There is no gain in using one hot in this case.
answered Nov 16 '18 at 14:01
Matthieu BrucherMatthieu Brucher
17.5k42345
add a comment |
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1 Answer
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Of course, that will be enough. There is no gain in using one hot in this case.
answered Nov 16 '18 at 14:01
Matthieu BrucherMatthieu Brucher
17.5k42345
add a comment |
Of course, that will be enough. There is no gain in using one hot in this case.
answered Nov 16 '18 at 14:01
Matthieu BrucherMatthieu Brucher
17.5k42345
add a comment |
Of course, that will be enough. There is no gain in using one hot in this case.
answered Nov 16 '18 at 14:01
Matthieu BrucherMatthieu Brucher
17.5k42345
Of course, that will be enough. There is no gain in using one hot in this case.
answered Nov 16 '18 at 14:01
Matthieu BrucherMatthieu Brucher
17.5k42345
answered Nov 16 '18 at 14:01
Matthieu BrucherMatthieu Brucher
17.5k42345
answered Nov 16 '18 at 14:01
Matthieu BrucherMatthieu Brucher
17.5k42345
answered Nov 16 '18 at 14:01
Matthieu BrucherMatthieu Brucher
17.5k42345
17.5k42345
add a comment |
add a comment |
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Read the accepted answer here
– AkshayNevrekar
Nov 16 '18 at 14:01