Change values from nominal to numeric
$begingroup$
I want to change the values of the class labels from nominal into numeric.
e.g if the values of a class are {iris-setosa,iris-virginica,iris-versicolor} i want to make them {0,1,2} so the instances will have as a value at the class label the form (0,0,1,2,0,1,1,2,0).
Any idea?
data-mining dataset dimensionality-reduction weka
asked Nov 11 '18 at 14:57
andrikoulasandrikoulas
1
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bumped to the homepage by Community♦ 15 mins ago
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$begingroup$
I want to change the values of the class labels from nominal into numeric.
e.g if the values of a class are {iris-setosa,iris-virginica,iris-versicolor} i want to make them {0,1,2} so the instances will have as a value at the class label the form (0,0,1,2,0,1,1,2,0).
Any idea?
data-mining dataset dimensionality-reduction weka
asked Nov 11 '18 at 14:57
andrikoulasandrikoulas
1
$endgroup$
bumped to the homepage by Community♦ 15 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 |
$begingroup$
I want to change the values of the class labels from nominal into numeric.
e.g if the values of a class are {iris-setosa,iris-virginica,iris-versicolor} i want to make them {0,1,2} so the instances will have as a value at the class label the form (0,0,1,2,0,1,1,2,0).
Any idea?
data-mining dataset dimensionality-reduction weka
asked Nov 11 '18 at 14:57
andrikoulasandrikoulas
1
$endgroup$
I want to change the values of the class labels from nominal into numeric.
e.g if the values of a class are {iris-setosa,iris-virginica,iris-versicolor} i want to make them {0,1,2} so the instances will have as a value at the class label the form (0,0,1,2,0,1,1,2,0).
Any idea?
data-mining dataset dimensionality-reduction weka
data-mining dataset dimensionality-reduction weka
asked Nov 11 '18 at 14:57
andrikoulasandrikoulas
1
asked Nov 11 '18 at 14:57
andrikoulasandrikoulas
1
asked Nov 11 '18 at 14:57
andrikoulasandrikoulas
1
asked Nov 11 '18 at 14:57
andrikoulasandrikoulas
1
asked Nov 11 '18 at 14:57
andrikoulasandrikoulas
1
1
bumped to the homepage by Community♦ 15 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♦ 15 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 |
1 Answer
1
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oldest
votes
$begingroup$
It's called Label Encoding
In python with the help of scikit-learn you can do following:
le = preprocessing.LabelEncoder() le.fit(["paris", "paris", "tokyo",
"amsterdam"])
list(le.classes_)
le.transform(["tokyo", "tokyo", "paris"])
list(le.inverse_transform([2, 2, 1]))
Documentation for more info: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html
PS: Label Encoding might be responsible for unintentional ordering of possible values. I.E. model might think that nominal value with associated numerical value 2 is more important than the nominal value with associated numerical value 1. The solution is One Hot Encoding.
answered Nov 11 '18 at 15:23
PreetPreet
4335
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1 Answer
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1 Answer
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$begingroup$
It's called Label Encoding
In python with the help of scikit-learn you can do following:
le = preprocessing.LabelEncoder() le.fit(["paris", "paris", "tokyo",
"amsterdam"])
list(le.classes_)
le.transform(["tokyo", "tokyo", "paris"])
list(le.inverse_transform([2, 2, 1]))
Documentation for more info: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html
PS: Label Encoding might be responsible for unintentional ordering of possible values. I.E. model might think that nominal value with associated numerical value 2 is more important than the nominal value with associated numerical value 1. The solution is One Hot Encoding.
answered Nov 11 '18 at 15:23
PreetPreet
4335
$endgroup$
add a comment |
$begingroup$
It's called Label Encoding
In python with the help of scikit-learn you can do following:
le = preprocessing.LabelEncoder() le.fit(["paris", "paris", "tokyo",
"amsterdam"])
list(le.classes_)
le.transform(["tokyo", "tokyo", "paris"])
list(le.inverse_transform([2, 2, 1]))
Documentation for more info: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html
PS: Label Encoding might be responsible for unintentional ordering of possible values. I.E. model might think that nominal value with associated numerical value 2 is more important than the nominal value with associated numerical value 1. The solution is One Hot Encoding.
answered Nov 11 '18 at 15:23
PreetPreet
4335
$endgroup$
add a comment |
$begingroup$
It's called Label Encoding
In python with the help of scikit-learn you can do following:
le = preprocessing.LabelEncoder() le.fit(["paris", "paris", "tokyo",
"amsterdam"])
list(le.classes_)
le.transform(["tokyo", "tokyo", "paris"])
list(le.inverse_transform([2, 2, 1]))
Documentation for more info: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html
PS: Label Encoding might be responsible for unintentional ordering of possible values. I.E. model might think that nominal value with associated numerical value 2 is more important than the nominal value with associated numerical value 1. The solution is One Hot Encoding.
answered Nov 11 '18 at 15:23
PreetPreet
4335
$endgroup$
It's called Label Encoding
In python with the help of scikit-learn you can do following:
le = preprocessing.LabelEncoder() le.fit(["paris", "paris", "tokyo",
"amsterdam"])
list(le.classes_)
le.transform(["tokyo", "tokyo", "paris"])
list(le.inverse_transform([2, 2, 1]))
Documentation for more info: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html
PS: Label Encoding might be responsible for unintentional ordering of possible values. I.E. model might think that nominal value with associated numerical value 2 is more important than the nominal value with associated numerical value 1. The solution is One Hot Encoding.
answered Nov 11 '18 at 15:23
PreetPreet
4335
answered Nov 11 '18 at 15:23
PreetPreet
4335
answered Nov 11 '18 at 15:23
PreetPreet
4335
answered Nov 11 '18 at 15:23
PreetPreet
4335
4335
add a comment |
add a comment |
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