Inverse Binary Feature
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I am feeding a binary value into my NN which represents whether the given example is a public holiday or not.
Is there a difference between assigning a 0 to public holidays and 1 to all other days or encoding it inversely?
If I am not mistaken, it should make no difference as the NN learns to assign corresponding weights/ bias anyway.
neural-network feature-selection feature-extraction feature-engineering
asked 2 days ago
1b151b15
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$begingroup$
I am feeding a binary value into my NN which represents whether the given example is a public holiday or not.
Is there a difference between assigning a 0 to public holidays and 1 to all other days or encoding it inversely?
If I am not mistaken, it should make no difference as the NN learns to assign corresponding weights/ bias anyway.
neural-network feature-selection feature-extraction feature-engineering
asked 2 days ago
1b151b15
183
New contributor
1b15 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 feeding a binary value into my NN which represents whether the given example is a public holiday or not.
Is there a difference between assigning a 0 to public holidays and 1 to all other days or encoding it inversely?
If I am not mistaken, it should make no difference as the NN learns to assign corresponding weights/ bias anyway.
neural-network feature-selection feature-extraction feature-engineering
asked 2 days ago
1b151b15
183
New contributor
1b15 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 feeding a binary value into my NN which represents whether the given example is a public holiday or not.
Is there a difference between assigning a 0 to public holidays and 1 to all other days or encoding it inversely?
If I am not mistaken, it should make no difference as the NN learns to assign corresponding weights/ bias anyway.
neural-network feature-selection feature-extraction feature-engineering
neural-network feature-selection feature-extraction feature-engineering
asked 2 days ago
1b151b15
183
New contributor
1b15 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
1b151b15
183
New contributor
1b15 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
1b151b15
183
New contributor
1b15 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
1b151b15
183
asked 2 days ago
1b151b15
183
183
New contributor
1b15 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
1b15 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
1b15 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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1 Answer
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Yes, it does not matter how you encode your features. Remember that bias is there for a reason, think about a simple perceptron with this weight and a ReLu activation function:
- f(x) = b + wx = 1 - 1*x
If you have x = 1 then f(1) = 0, and no signal will pass. However, if you have a x = 0 then f(0) = 1 and we will have a signal flowing on our network. Therefore, your network will learn the appropriate parameters to classify your data correctly based on a loss function given.
I hope this helps.
answered 2 days ago
Victor OliveiraVictor Oliveira
1114
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1 Answer
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1 Answer
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$begingroup$
Yes, it does not matter how you encode your features. Remember that bias is there for a reason, think about a simple perceptron with this weight and a ReLu activation function:
- f(x) = b + wx = 1 - 1*x
If you have x = 1 then f(1) = 0, and no signal will pass. However, if you have a x = 0 then f(0) = 1 and we will have a signal flowing on our network. Therefore, your network will learn the appropriate parameters to classify your data correctly based on a loss function given.
I hope this helps.
answered 2 days ago
Victor OliveiraVictor Oliveira
1114
$endgroup$
add a comment |
$begingroup$
Yes, it does not matter how you encode your features. Remember that bias is there for a reason, think about a simple perceptron with this weight and a ReLu activation function:
- f(x) = b + wx = 1 - 1*x
If you have x = 1 then f(1) = 0, and no signal will pass. However, if you have a x = 0 then f(0) = 1 and we will have a signal flowing on our network. Therefore, your network will learn the appropriate parameters to classify your data correctly based on a loss function given.
I hope this helps.
answered 2 days ago
Victor OliveiraVictor Oliveira
1114
$endgroup$
add a comment |
$begingroup$
Yes, it does not matter how you encode your features. Remember that bias is there for a reason, think about a simple perceptron with this weight and a ReLu activation function:
- f(x) = b + wx = 1 - 1*x
If you have x = 1 then f(1) = 0, and no signal will pass. However, if you have a x = 0 then f(0) = 1 and we will have a signal flowing on our network. Therefore, your network will learn the appropriate parameters to classify your data correctly based on a loss function given.
I hope this helps.
answered 2 days ago
Victor OliveiraVictor Oliveira
1114
$endgroup$
Yes, it does not matter how you encode your features. Remember that bias is there for a reason, think about a simple perceptron with this weight and a ReLu activation function:
- f(x) = b + wx = 1 - 1*x
If you have x = 1 then f(1) = 0, and no signal will pass. However, if you have a x = 0 then f(0) = 1 and we will have a signal flowing on our network. Therefore, your network will learn the appropriate parameters to classify your data correctly based on a loss function given.
I hope this helps.
answered 2 days ago
Victor OliveiraVictor Oliveira
1114
answered 2 days ago
Victor OliveiraVictor Oliveira
1114
answered 2 days ago
Victor OliveiraVictor Oliveira
1114
answered 2 days ago
Victor OliveiraVictor Oliveira
1114
1114
add a comment |
add a comment |
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