PCA-like analysis for dataset that has both categorical and continuous variables
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I have a dataset containing a categorical variable and multiple continuous variables. The categorical variables are coded as discrete integers, whereas the continuous variables are just a range of floats. I believe that the variance in my dataset can be almost entirely described by the single categorical variable and one of the many continuous variables. To justify this, I would be interested in using PCA, but I'm not sure the best approach to use when I am considering categorical data. Any suggestions?
dataset statistics
asked Sep 19 '18 at 15:52
AndrewAndrew
1
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bumped to the homepage by Community♦ 39 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 |
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I have a dataset containing a categorical variable and multiple continuous variables. The categorical variables are coded as discrete integers, whereas the continuous variables are just a range of floats. I believe that the variance in my dataset can be almost entirely described by the single categorical variable and one of the many continuous variables. To justify this, I would be interested in using PCA, but I'm not sure the best approach to use when I am considering categorical data. Any suggestions?
dataset statistics
asked Sep 19 '18 at 15:52
AndrewAndrew
1
$endgroup$
bumped to the homepage by Community♦ 39 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
PCA requires you to be able to define meaningful distances between categories.
$endgroup$
– oW_
Dec 21 '18 at 18:42
add a comment |
$begingroup$
I have a dataset containing a categorical variable and multiple continuous variables. The categorical variables are coded as discrete integers, whereas the continuous variables are just a range of floats. I believe that the variance in my dataset can be almost entirely described by the single categorical variable and one of the many continuous variables. To justify this, I would be interested in using PCA, but I'm not sure the best approach to use when I am considering categorical data. Any suggestions?
dataset statistics
asked Sep 19 '18 at 15:52
AndrewAndrew
1
$endgroup$
I have a dataset containing a categorical variable and multiple continuous variables. The categorical variables are coded as discrete integers, whereas the continuous variables are just a range of floats. I believe that the variance in my dataset can be almost entirely described by the single categorical variable and one of the many continuous variables. To justify this, I would be interested in using PCA, but I'm not sure the best approach to use when I am considering categorical data. Any suggestions?
dataset statistics
dataset statistics
asked Sep 19 '18 at 15:52
AndrewAndrew
1
asked Sep 19 '18 at 15:52
AndrewAndrew
1
asked Sep 19 '18 at 15:52
AndrewAndrew
1
asked Sep 19 '18 at 15:52
AndrewAndrew
1
asked Sep 19 '18 at 15:52
AndrewAndrew
1
1
bumped to the homepage by Community♦ 39 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♦ 39 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
PCA requires you to be able to define meaningful distances between categories.
$endgroup$
– oW_
Dec 21 '18 at 18:42
add a comment |
$begingroup$
PCA requires you to be able to define meaningful distances between categories.
$endgroup$
– oW_
Dec 21 '18 at 18:42
$begingroup$
PCA requires you to be able to define meaningful distances between categories.
$endgroup$
– oW_
Dec 21 '18 at 18:42
$begingroup$
PCA requires you to be able to define meaningful distances between categories.
$endgroup$
– oW_
Dec 21 '18 at 18:42
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
How many values can the categorical value take?
Maybe make a column for each possible value and have 1 if the column name matches the categorical value, 0 otherwise.
I think that will show up in PCA.
answered Sep 19 '18 at 19:34
Pieter21Pieter21
51626
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add a comment |
$begingroup$
I'm not aware of any the dimensionality reduction algorithms (like PCA) that can work with categorical values.
However, an approach that could help you with that is to make a one-hot encoding of your categorical variables (if the number of possible values is manageable. Otherwise, try to pick only the most frequent values and assign the rest to a single variable).
If you are using Pandas DataFrames, get_dummies can be helpful.
answered Dec 21 '18 at 9:09
Arthur CamaraArthur Camara
101
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
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active
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active
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$begingroup$
How many values can the categorical value take?
Maybe make a column for each possible value and have 1 if the column name matches the categorical value, 0 otherwise.
I think that will show up in PCA.
answered Sep 19 '18 at 19:34
Pieter21Pieter21
51626
$endgroup$
add a comment |
$begingroup$
How many values can the categorical value take?
Maybe make a column for each possible value and have 1 if the column name matches the categorical value, 0 otherwise.
I think that will show up in PCA.
answered Sep 19 '18 at 19:34
Pieter21Pieter21
51626
$endgroup$
add a comment |
$begingroup$
How many values can the categorical value take?
Maybe make a column for each possible value and have 1 if the column name matches the categorical value, 0 otherwise.
I think that will show up in PCA.
answered Sep 19 '18 at 19:34
Pieter21Pieter21
51626
$endgroup$
How many values can the categorical value take?
Maybe make a column for each possible value and have 1 if the column name matches the categorical value, 0 otherwise.
I think that will show up in PCA.
answered Sep 19 '18 at 19:34
Pieter21Pieter21
51626
answered Sep 19 '18 at 19:34
Pieter21Pieter21
51626
answered Sep 19 '18 at 19:34
Pieter21Pieter21
51626
answered Sep 19 '18 at 19:34
Pieter21Pieter21
51626
51626
add a comment |
add a comment |
$begingroup$
I'm not aware of any the dimensionality reduction algorithms (like PCA) that can work with categorical values.
However, an approach that could help you with that is to make a one-hot encoding of your categorical variables (if the number of possible values is manageable. Otherwise, try to pick only the most frequent values and assign the rest to a single variable).
If you are using Pandas DataFrames, get_dummies can be helpful.
answered Dec 21 '18 at 9:09
Arthur CamaraArthur Camara
101
$endgroup$
add a comment |
$begingroup$
I'm not aware of any the dimensionality reduction algorithms (like PCA) that can work with categorical values.
However, an approach that could help you with that is to make a one-hot encoding of your categorical variables (if the number of possible values is manageable. Otherwise, try to pick only the most frequent values and assign the rest to a single variable).
If you are using Pandas DataFrames, get_dummies can be helpful.
answered Dec 21 '18 at 9:09
Arthur CamaraArthur Camara
101
$endgroup$
add a comment |
$begingroup$
I'm not aware of any the dimensionality reduction algorithms (like PCA) that can work with categorical values.
However, an approach that could help you with that is to make a one-hot encoding of your categorical variables (if the number of possible values is manageable. Otherwise, try to pick only the most frequent values and assign the rest to a single variable).
If you are using Pandas DataFrames, get_dummies can be helpful.
answered Dec 21 '18 at 9:09
Arthur CamaraArthur Camara
101
$endgroup$
I'm not aware of any the dimensionality reduction algorithms (like PCA) that can work with categorical values.
However, an approach that could help you with that is to make a one-hot encoding of your categorical variables (if the number of possible values is manageable. Otherwise, try to pick only the most frequent values and assign the rest to a single variable).
If you are using Pandas DataFrames, get_dummies can be helpful.
answered Dec 21 '18 at 9:09
Arthur CamaraArthur Camara
101
answered Dec 21 '18 at 9:09
Arthur CamaraArthur Camara
101
answered Dec 21 '18 at 9:09
Arthur CamaraArthur Camara
101
answered Dec 21 '18 at 9:09
Arthur CamaraArthur Camara
101
101
add a comment |
add a comment |
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$begingroup$
PCA requires you to be able to define meaningful distances between categories.
$endgroup$
– oW_
Dec 21 '18 at 18:42