Training a classifier when some of the features are unknown
$begingroup$
I am training a classifier in Matlab with a dataset that I created.
Unfortunately some of the features in the dataset were not recorded.
I currently have the unknown features set as -99999.
So, for example my dataset looks something like this:
class1: 10 1 12 -99999 6 8
class2: 5 -99999 4 3 2 -99999
class3: 18 2 11 22 7 5
...
and so on, where the -99999 are the places where the features werent able to be measured. In this case, each class has 6 features.
I don't want to bias my classifier with the unknown features so I thought it would be a good idea to set the unknowns to -99999 so it would be way out of the range of normal features.
I tested the classifier with the -99999's and it was 78% accurate.
Then I changed the -99999 to 0's and tested the classifier again, this time it was 91% accurate.
So my question is, what is a general rule for training a classifier when some of the features were not recorded? Was I right to assume setting the unknowns to a very high negative value? But why was it more accurate when I set the unknowns to 0s?
Thanks for reading!
machine-learning classification dataset matlab
asked 3 mins ago
Darklink9110Darklink9110
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$begingroup$
I am training a classifier in Matlab with a dataset that I created.
Unfortunately some of the features in the dataset were not recorded.
I currently have the unknown features set as -99999.
So, for example my dataset looks something like this:
class1: 10 1 12 -99999 6 8
class2: 5 -99999 4 3 2 -99999
class3: 18 2 11 22 7 5
...
and so on, where the -99999 are the places where the features werent able to be measured. In this case, each class has 6 features.
I don't want to bias my classifier with the unknown features so I thought it would be a good idea to set the unknowns to -99999 so it would be way out of the range of normal features.
I tested the classifier with the -99999's and it was 78% accurate.
Then I changed the -99999 to 0's and tested the classifier again, this time it was 91% accurate.
So my question is, what is a general rule for training a classifier when some of the features were not recorded? Was I right to assume setting the unknowns to a very high negative value? But why was it more accurate when I set the unknowns to 0s?
Thanks for reading!
machine-learning classification dataset matlab
asked 3 mins ago
Darklink9110Darklink9110
11
New contributor
Darklink9110 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am training a classifier in Matlab with a dataset that I created.
Unfortunately some of the features in the dataset were not recorded.
I currently have the unknown features set as -99999.
So, for example my dataset looks something like this:
class1: 10 1 12 -99999 6 8
class2: 5 -99999 4 3 2 -99999
class3: 18 2 11 22 7 5
...
and so on, where the -99999 are the places where the features werent able to be measured. In this case, each class has 6 features.
I don't want to bias my classifier with the unknown features so I thought it would be a good idea to set the unknowns to -99999 so it would be way out of the range of normal features.
I tested the classifier with the -99999's and it was 78% accurate.
Then I changed the -99999 to 0's and tested the classifier again, this time it was 91% accurate.
So my question is, what is a general rule for training a classifier when some of the features were not recorded? Was I right to assume setting the unknowns to a very high negative value? But why was it more accurate when I set the unknowns to 0s?
Thanks for reading!
machine-learning classification dataset matlab
asked 3 mins ago
Darklink9110Darklink9110
11
New contributor
Darklink9110 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am training a classifier in Matlab with a dataset that I created.
Unfortunately some of the features in the dataset were not recorded.
I currently have the unknown features set as -99999.
So, for example my dataset looks something like this:
class1: 10 1 12 -99999 6 8
class2: 5 -99999 4 3 2 -99999
class3: 18 2 11 22 7 5
...
and so on, where the -99999 are the places where the features werent able to be measured. In this case, each class has 6 features.
I don't want to bias my classifier with the unknown features so I thought it would be a good idea to set the unknowns to -99999 so it would be way out of the range of normal features.
I tested the classifier with the -99999's and it was 78% accurate.
Then I changed the -99999 to 0's and tested the classifier again, this time it was 91% accurate.
So my question is, what is a general rule for training a classifier when some of the features were not recorded? Was I right to assume setting the unknowns to a very high negative value? But why was it more accurate when I set the unknowns to 0s?
Thanks for reading!
machine-learning classification dataset matlab
machine-learning classification dataset matlab
asked 3 mins ago
Darklink9110Darklink9110
11
New contributor
Darklink9110 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
Darklink9110Darklink9110
11
New contributor
Darklink9110 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
Darklink9110Darklink9110
11
New contributor
Darklink9110 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
Darklink9110Darklink9110
11
asked 3 mins ago
Darklink9110Darklink9110
11
11
New contributor
Darklink9110 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Darklink9110 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Darklink9110 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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