Problem with important feature having a lot of missing value
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I am facing a dilemma with a project of mine. One of the variables (numerical) doesn't have enough data i,e almost 99% data are missing. However, upon talking to the domain experts, it appears that the particular variable is important to the problem we are trying to solve (model). Initially, I thought of converting it to a binary variable such that 1 will represent that the variable has a value at that position and 0 will represent the missing value. However, it seems that we are missing information by doing it.
Can anybody suggest any way go forward?
One thought came to me is to discretize the variables using quantiles, but then what to do with the missing values?
Another one is to include both the binary variable along with the original variable in the model with missing values replaced by some imputed values. But I cannot come to any logical reasoning as to why or why not this will work.
Any light on this matter would be greatly helpful (other than of course drop it altogether).
Thanks.
machine-learning feature-engineering
asked 5 hours ago
user62198user62198
4612617
$endgroup$
add a comment |
$begingroup$
I am facing a dilemma with a project of mine. One of the variables (numerical) doesn't have enough data i,e almost 99% data are missing. However, upon talking to the domain experts, it appears that the particular variable is important to the problem we are trying to solve (model). Initially, I thought of converting it to a binary variable such that 1 will represent that the variable has a value at that position and 0 will represent the missing value. However, it seems that we are missing information by doing it.
Can anybody suggest any way go forward?
One thought came to me is to discretize the variables using quantiles, but then what to do with the missing values?
Another one is to include both the binary variable along with the original variable in the model with missing values replaced by some imputed values. But I cannot come to any logical reasoning as to why or why not this will work.
Any light on this matter would be greatly helpful (other than of course drop it altogether).
Thanks.
machine-learning feature-engineering
asked 5 hours ago
user62198user62198
4612617
$endgroup$
add a comment |
$begingroup$
I am facing a dilemma with a project of mine. One of the variables (numerical) doesn't have enough data i,e almost 99% data are missing. However, upon talking to the domain experts, it appears that the particular variable is important to the problem we are trying to solve (model). Initially, I thought of converting it to a binary variable such that 1 will represent that the variable has a value at that position and 0 will represent the missing value. However, it seems that we are missing information by doing it.
Can anybody suggest any way go forward?
One thought came to me is to discretize the variables using quantiles, but then what to do with the missing values?
Another one is to include both the binary variable along with the original variable in the model with missing values replaced by some imputed values. But I cannot come to any logical reasoning as to why or why not this will work.
Any light on this matter would be greatly helpful (other than of course drop it altogether).
Thanks.
machine-learning feature-engineering
asked 5 hours ago
user62198user62198
4612617
$endgroup$
I am facing a dilemma with a project of mine. One of the variables (numerical) doesn't have enough data i,e almost 99% data are missing. However, upon talking to the domain experts, it appears that the particular variable is important to the problem we are trying to solve (model). Initially, I thought of converting it to a binary variable such that 1 will represent that the variable has a value at that position and 0 will represent the missing value. However, it seems that we are missing information by doing it.
Can anybody suggest any way go forward?
One thought came to me is to discretize the variables using quantiles, but then what to do with the missing values?
Another one is to include both the binary variable along with the original variable in the model with missing values replaced by some imputed values. But I cannot come to any logical reasoning as to why or why not this will work.
Any light on this matter would be greatly helpful (other than of course drop it altogether).
Thanks.
machine-learning feature-engineering
machine-learning feature-engineering
asked 5 hours ago
user62198user62198
4612617
asked 5 hours ago
user62198user62198
4612617
asked 5 hours ago
user62198user62198
4612617
asked 5 hours ago
user62198user62198
4612617
asked 5 hours ago
user62198user62198
4612617
4612617
add a comment |
add a comment |
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