How to encode data with a feature having multidimensional features (colors)?
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My dataset has around 20 features, one of which is colors(in string format). There are around 50 different colors. I have converted them to RGB, but now I want to encode the data in such a way that the values are relevant, because I will cluster the data on this feature later on. To do this, one-hot encoding proved ineffective. Suggestions?
python clustering encoding
asked Jul 2 '18 at 10:37
Yash GandheYash Gandhe
33
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bumped to the homepage by Community♦ 7 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
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$begingroup$
My dataset has around 20 features, one of which is colors(in string format). There are around 50 different colors. I have converted them to RGB, but now I want to encode the data in such a way that the values are relevant, because I will cluster the data on this feature later on. To do this, one-hot encoding proved ineffective. Suggestions?
python clustering encoding
asked Jul 2 '18 at 10:37
Yash GandheYash Gandhe
33
$endgroup$
bumped to the homepage by Community♦ 7 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
My dataset has around 20 features, one of which is colors(in string format). There are around 50 different colors. I have converted them to RGB, but now I want to encode the data in such a way that the values are relevant, because I will cluster the data on this feature later on. To do this, one-hot encoding proved ineffective. Suggestions?
python clustering encoding
asked Jul 2 '18 at 10:37
Yash GandheYash Gandhe
33
$endgroup$
My dataset has around 20 features, one of which is colors(in string format). There are around 50 different colors. I have converted them to RGB, but now I want to encode the data in such a way that the values are relevant, because I will cluster the data on this feature later on. To do this, one-hot encoding proved ineffective. Suggestions?
python clustering encoding
python clustering encoding
asked Jul 2 '18 at 10:37
Yash GandheYash Gandhe
33
asked Jul 2 '18 at 10:37
Yash GandheYash Gandhe
33
asked Jul 2 '18 at 10:37
Yash GandheYash Gandhe
33
asked Jul 2 '18 at 10:37
Yash GandheYash Gandhe
33
asked Jul 2 '18 at 10:37
Yash GandheYash Gandhe
33
33
bumped to the homepage by Community♦ 7 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 7 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
2 Answers
2
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One-hot encoding will give you a sparse matrix, Try LabelEncoding, before converting them into RGB that is.
Also, you could try breaking the RGB values into three features (R, G, B) and try that approach as well.
Hope this helps.
answered Jul 2 '18 at 10:47
tenshitenshi
5166
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add a comment |
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Depends on what the Colour means in your data. Example temperature can be colour from blue and red, in which case 1-hot won't work. But looks like colour is categorical in your data. In that case it depends on what you are using to classify the data, are you using a decision tree, or SVM etc. Cos in case of decision tree you don't have to do anything as it can take care of categorical data ( again depends on which implementation you use ). in case of other methods they have their own ways in which categorical data has to be handled.
answered Aug 22 '18 at 9:22
Adithya SamaAdithya Sama
287
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add a comment |
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2 Answers
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2 Answers
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$begingroup$
One-hot encoding will give you a sparse matrix, Try LabelEncoding, before converting them into RGB that is.
Also, you could try breaking the RGB values into three features (R, G, B) and try that approach as well.
Hope this helps.
answered Jul 2 '18 at 10:47
tenshitenshi
5166
$endgroup$
add a comment |
$begingroup$
One-hot encoding will give you a sparse matrix, Try LabelEncoding, before converting them into RGB that is.
Also, you could try breaking the RGB values into three features (R, G, B) and try that approach as well.
Hope this helps.
answered Jul 2 '18 at 10:47
tenshitenshi
5166
$endgroup$
add a comment |
$begingroup$
One-hot encoding will give you a sparse matrix, Try LabelEncoding, before converting them into RGB that is.
Also, you could try breaking the RGB values into three features (R, G, B) and try that approach as well.
Hope this helps.
answered Jul 2 '18 at 10:47
tenshitenshi
5166
$endgroup$
One-hot encoding will give you a sparse matrix, Try LabelEncoding, before converting them into RGB that is.
Also, you could try breaking the RGB values into three features (R, G, B) and try that approach as well.
Hope this helps.
answered Jul 2 '18 at 10:47
tenshitenshi
5166
answered Jul 2 '18 at 10:47
tenshitenshi
5166
answered Jul 2 '18 at 10:47
tenshitenshi
5166
answered Jul 2 '18 at 10:47
tenshitenshi
5166
5166
add a comment |
add a comment |
$begingroup$
Depends on what the Colour means in your data. Example temperature can be colour from blue and red, in which case 1-hot won't work. But looks like colour is categorical in your data. In that case it depends on what you are using to classify the data, are you using a decision tree, or SVM etc. Cos in case of decision tree you don't have to do anything as it can take care of categorical data ( again depends on which implementation you use ). in case of other methods they have their own ways in which categorical data has to be handled.
answered Aug 22 '18 at 9:22
Adithya SamaAdithya Sama
287
$endgroup$
add a comment |
$begingroup$
Depends on what the Colour means in your data. Example temperature can be colour from blue and red, in which case 1-hot won't work. But looks like colour is categorical in your data. In that case it depends on what you are using to classify the data, are you using a decision tree, or SVM etc. Cos in case of decision tree you don't have to do anything as it can take care of categorical data ( again depends on which implementation you use ). in case of other methods they have their own ways in which categorical data has to be handled.
answered Aug 22 '18 at 9:22
Adithya SamaAdithya Sama
287
$endgroup$
add a comment |
$begingroup$
Depends on what the Colour means in your data. Example temperature can be colour from blue and red, in which case 1-hot won't work. But looks like colour is categorical in your data. In that case it depends on what you are using to classify the data, are you using a decision tree, or SVM etc. Cos in case of decision tree you don't have to do anything as it can take care of categorical data ( again depends on which implementation you use ). in case of other methods they have their own ways in which categorical data has to be handled.
answered Aug 22 '18 at 9:22
Adithya SamaAdithya Sama
287
$endgroup$
Depends on what the Colour means in your data. Example temperature can be colour from blue and red, in which case 1-hot won't work. But looks like colour is categorical in your data. In that case it depends on what you are using to classify the data, are you using a decision tree, or SVM etc. Cos in case of decision tree you don't have to do anything as it can take care of categorical data ( again depends on which implementation you use ). in case of other methods they have their own ways in which categorical data has to be handled.
answered Aug 22 '18 at 9:22
Adithya SamaAdithya Sama
287
answered Aug 22 '18 at 9:22
Adithya SamaAdithya Sama
287
answered Aug 22 '18 at 9:22
Adithya SamaAdithya Sama
287
answered Aug 22 '18 at 9:22
Adithya SamaAdithya Sama
287
287
add a comment |
add a comment |
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