How to encode data with a feature having multidimensional features (colors)? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsWords to numbers faster lookupPreparing, Scaling and Selecting from a combination of numerical and categorical featuresWhat approach for creating a multi-classification model based on all categorical features (1 with 5,000 levels)?How to use multiple encoders(one-hot and numerical) together for PCAHow to implement feature selection for categorical variables (especially with many categories)?What happens if I do not encode the lables or classifiers in the data?How to use one hot encoding of string categorical features in keras?How to pass 2 features to LSTM , one of them is one-hot-encoded with Keras?python: why add new features of image to be worse result with xgboost
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How to encode data with a feature having multidimensional features (colors)?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsWords to numbers faster lookupPreparing, Scaling and Selecting from a combination of numerical and categorical featuresWhat approach for creating a multi-classification model based on all categorical features (1 with 5,000 levels)?How to use multiple encoders(one-hot and numerical) together for PCAHow to implement feature selection for categorical variables (especially with many categories)?What happens if I do not encode the lables or classifiers in the data?How to use one hot encoding of string categorical features in keras?How to pass 2 features to LSTM , one of them is one-hot-encoded with Keras?python: why add new features of image to be worse result with xgboost
$begingroup$
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
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bumped to the homepage by Community♦ 35 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
$endgroup$
bumped to the homepage by Community♦ 35 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♦ 35 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♦ 35 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
active
oldest
votes
$begingroup$
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 |
$begingroup$
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 |
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2 Answers
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active
oldest
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2 Answers
2
active
oldest
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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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