Handling missing values to optimize polynomial features Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsHandling many missing valuesMissing Categorical Features - no imputationFilling missing values for important featuresMissing population values in census dataHow would one impute missing values for a Discrete variable?Imputation missing values other than using Mean, Median in pythonDealing with NaN (missing) values for Logistic Regression- Best practices?Missing Values in ClassificationMissing Values In New DataHow to deal with missing data for Bernoulli Naive Bayes?
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Handling missing values to optimize polynomial features
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsHandling many missing valuesMissing Categorical Features - no imputationFilling missing values for important featuresMissing population values in census dataHow would one impute missing values for a Discrete variable?Imputation missing values other than using Mean, Median in pythonDealing with NaN (missing) values for Logistic Regression- Best practices?Missing Values in ClassificationMissing Values In New DataHow to deal with missing data for Bernoulli Naive Bayes?
$begingroup$
I was playing around with some data to practice my Python and machine learning skills and wanted to create polynomial features from two features that I think are related and have a strong influence on the predicted output.
Unfortunately my data has missing values (np.NaN) and sklearn's PolynomialFeatures() can not handle these values. What is the best way to impute these values?
I've been trying to replace them with 0, 1, mean and median and for my dataset using the median seems to be the best solution. But can this be generalized and what is the intuition behind it?
I was also wondering if filling methods like ffill, bfill or even KNN modelling can be useful in this context.
Thanks a lot!
feature-engineering missing-data data-imputation
asked Oct 21 '18 at 8:47
Steven Van DorpeSteven Van Dorpe
162
$endgroup$
add a comment |
$begingroup$
I was playing around with some data to practice my Python and machine learning skills and wanted to create polynomial features from two features that I think are related and have a strong influence on the predicted output.
Unfortunately my data has missing values (np.NaN) and sklearn's PolynomialFeatures() can not handle these values. What is the best way to impute these values?
I've been trying to replace them with 0, 1, mean and median and for my dataset using the median seems to be the best solution. But can this be generalized and what is the intuition behind it?
I was also wondering if filling methods like ffill, bfill or even KNN modelling can be useful in this context.
Thanks a lot!
feature-engineering missing-data data-imputation
asked Oct 21 '18 at 8:47
Steven Van DorpeSteven Van Dorpe
162
$endgroup$
add a comment |
$begingroup$
I was playing around with some data to practice my Python and machine learning skills and wanted to create polynomial features from two features that I think are related and have a strong influence on the predicted output.
Unfortunately my data has missing values (np.NaN) and sklearn's PolynomialFeatures() can not handle these values. What is the best way to impute these values?
I've been trying to replace them with 0, 1, mean and median and for my dataset using the median seems to be the best solution. But can this be generalized and what is the intuition behind it?
I was also wondering if filling methods like ffill, bfill or even KNN modelling can be useful in this context.
Thanks a lot!
feature-engineering missing-data data-imputation
asked Oct 21 '18 at 8:47
Steven Van DorpeSteven Van Dorpe
162
$endgroup$
I was playing around with some data to practice my Python and machine learning skills and wanted to create polynomial features from two features that I think are related and have a strong influence on the predicted output.
Unfortunately my data has missing values (np.NaN) and sklearn's PolynomialFeatures() can not handle these values. What is the best way to impute these values?
I've been trying to replace them with 0, 1, mean and median and for my dataset using the median seems to be the best solution. But can this be generalized and what is the intuition behind it?
I was also wondering if filling methods like ffill, bfill or even KNN modelling can be useful in this context.
Thanks a lot!
feature-engineering missing-data data-imputation
feature-engineering missing-data data-imputation
asked Oct 21 '18 at 8:47
Steven Van DorpeSteven Van Dorpe
162
asked Oct 21 '18 at 8:47
Steven Van DorpeSteven Van Dorpe
162
asked Oct 21 '18 at 8:47
Steven Van DorpeSteven Van Dorpe
162
asked Oct 21 '18 at 8:47
Steven Van DorpeSteven Van Dorpe
162
asked Oct 21 '18 at 8:47
Steven Van DorpeSteven Van Dorpe
162
162
add a comment |
add a comment |
1 Answer
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$begingroup$
There is no globally - one could say even locally - ideal way to deal with missing data. This aspect points to incompleteness in the data you're feeding your algorithms, and imputing is simply a technique meant to fill gaps.
Data imputation's motivation is to make feature distribution in your sets the closest possible to whatever real-world distribution it attempts to portray.
The intuition behind why the median is what worked better for your scenario is not something I could be precise about without having access to the data you worked upon, but the positive result is deeply related to your feature distribution, which for the missing points in your data is better represented by the feature's median than all other metrics you've calculated. I'd recommend reading material such as this article that both explains and shows implementation of different imputation techniques - KNN, as you mentioned, being one of them. One of the advantages is seeing how different methods work for a given distribution:
As you have already pointed in the end of your question, methods such as KNN are some straightforward means of imputation that could benefit your case better than mean/median imputation. The biggest difference between those is that KNN better preserves the variance in your data, whereas mean imputation (as you can see from the above image) shifts missing data towards a single value.
Since no method is 100% globally optimal, I'd advise you to try them - KNN, Multiple Interpolation and the likes - and compare. Invest the appropriate amount of time on the techniques that make the most sense for your data.
answered 36 mins ago
jcezarmsjcezarms
515
New contributor
jcezarms is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
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1 Answer
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1 Answer
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$begingroup$
There is no globally - one could say even locally - ideal way to deal with missing data. This aspect points to incompleteness in the data you're feeding your algorithms, and imputing is simply a technique meant to fill gaps.
Data imputation's motivation is to make feature distribution in your sets the closest possible to whatever real-world distribution it attempts to portray.
The intuition behind why the median is what worked better for your scenario is not something I could be precise about without having access to the data you worked upon, but the positive result is deeply related to your feature distribution, which for the missing points in your data is better represented by the feature's median than all other metrics you've calculated. I'd recommend reading material such as this article that both explains and shows implementation of different imputation techniques - KNN, as you mentioned, being one of them. One of the advantages is seeing how different methods work for a given distribution:
As you have already pointed in the end of your question, methods such as KNN are some straightforward means of imputation that could benefit your case better than mean/median imputation. The biggest difference between those is that KNN better preserves the variance in your data, whereas mean imputation (as you can see from the above image) shifts missing data towards a single value.
Since no method is 100% globally optimal, I'd advise you to try them - KNN, Multiple Interpolation and the likes - and compare. Invest the appropriate amount of time on the techniques that make the most sense for your data.
answered 36 mins ago
jcezarmsjcezarms
515
New contributor
jcezarms 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 |
$begingroup$
There is no globally - one could say even locally - ideal way to deal with missing data. This aspect points to incompleteness in the data you're feeding your algorithms, and imputing is simply a technique meant to fill gaps.
Data imputation's motivation is to make feature distribution in your sets the closest possible to whatever real-world distribution it attempts to portray.
The intuition behind why the median is what worked better for your scenario is not something I could be precise about without having access to the data you worked upon, but the positive result is deeply related to your feature distribution, which for the missing points in your data is better represented by the feature's median than all other metrics you've calculated. I'd recommend reading material such as this article that both explains and shows implementation of different imputation techniques - KNN, as you mentioned, being one of them. One of the advantages is seeing how different methods work for a given distribution:
As you have already pointed in the end of your question, methods such as KNN are some straightforward means of imputation that could benefit your case better than mean/median imputation. The biggest difference between those is that KNN better preserves the variance in your data, whereas mean imputation (as you can see from the above image) shifts missing data towards a single value.
Since no method is 100% globally optimal, I'd advise you to try them - KNN, Multiple Interpolation and the likes - and compare. Invest the appropriate amount of time on the techniques that make the most sense for your data.
answered 36 mins ago
jcezarmsjcezarms
515
New contributor
jcezarms 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 |
$begingroup$
There is no globally - one could say even locally - ideal way to deal with missing data. This aspect points to incompleteness in the data you're feeding your algorithms, and imputing is simply a technique meant to fill gaps.
Data imputation's motivation is to make feature distribution in your sets the closest possible to whatever real-world distribution it attempts to portray.
The intuition behind why the median is what worked better for your scenario is not something I could be precise about without having access to the data you worked upon, but the positive result is deeply related to your feature distribution, which for the missing points in your data is better represented by the feature's median than all other metrics you've calculated. I'd recommend reading material such as this article that both explains and shows implementation of different imputation techniques - KNN, as you mentioned, being one of them. One of the advantages is seeing how different methods work for a given distribution:
As you have already pointed in the end of your question, methods such as KNN are some straightforward means of imputation that could benefit your case better than mean/median imputation. The biggest difference between those is that KNN better preserves the variance in your data, whereas mean imputation (as you can see from the above image) shifts missing data towards a single value.
Since no method is 100% globally optimal, I'd advise you to try them - KNN, Multiple Interpolation and the likes - and compare. Invest the appropriate amount of time on the techniques that make the most sense for your data.
answered 36 mins ago
jcezarmsjcezarms
515
New contributor
jcezarms is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
There is no globally - one could say even locally - ideal way to deal with missing data. This aspect points to incompleteness in the data you're feeding your algorithms, and imputing is simply a technique meant to fill gaps.
Data imputation's motivation is to make feature distribution in your sets the closest possible to whatever real-world distribution it attempts to portray.
The intuition behind why the median is what worked better for your scenario is not something I could be precise about without having access to the data you worked upon, but the positive result is deeply related to your feature distribution, which for the missing points in your data is better represented by the feature's median than all other metrics you've calculated. I'd recommend reading material such as this article that both explains and shows implementation of different imputation techniques - KNN, as you mentioned, being one of them. One of the advantages is seeing how different methods work for a given distribution:
As you have already pointed in the end of your question, methods such as KNN are some straightforward means of imputation that could benefit your case better than mean/median imputation. The biggest difference between those is that KNN better preserves the variance in your data, whereas mean imputation (as you can see from the above image) shifts missing data towards a single value.
Since no method is 100% globally optimal, I'd advise you to try them - KNN, Multiple Interpolation and the likes - and compare. Invest the appropriate amount of time on the techniques that make the most sense for your data.
answered 36 mins ago
jcezarmsjcezarms
515
New contributor
jcezarms is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 36 mins ago
jcezarmsjcezarms
515
New contributor
jcezarms is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 36 mins ago
jcezarmsjcezarms
515
answered 36 mins ago
jcezarmsjcezarms
515
515
New contributor
jcezarms is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
jcezarms is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
jcezarms is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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