Decision Tree status column & related numerical value column The Next CEO of Stack OverflowHow to handle catagorical data while training decision tree using scikit-learn/ sklearn?Feature Importance extraction of Decision Trees (scikit-learn)decision trees from features of multiple datatypesDecision Tree produces different outputsPassing categorical data to Sklearn Decision TreeDecision tree algorithm for mixed numeric and nominal dataUse of one-hot encoder to build decision treesIn sklearn, how can one-hot encoding help when building decision tree with categorical features?Using OneHotEncoder for categorical features in decision tree classifierWhat is relation between R-squared and numerical data in case of Decision Tree?
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Decision Tree status column & related numerical value column
The Next CEO of Stack OverflowHow to handle catagorical data while training decision tree using scikit-learn/ sklearn?Feature Importance extraction of Decision Trees (scikit-learn)decision trees from features of multiple datatypesDecision Tree produces different outputsPassing categorical data to Sklearn Decision TreeDecision tree algorithm for mixed numeric and nominal dataUse of one-hot encoder to build decision treesIn sklearn, how can one-hot encoding help when building decision tree with categorical features?Using OneHotEncoder for categorical features in decision tree classifierWhat is relation between R-squared and numerical data in case of Decision Tree?
I have a data including two columns where one is categorically shows the status of the feature & the other one numerically shows the related value. Just like below:
I want to run a decision tree algorithm via scikit learn on this data. I am not sure how to deal with these two columns because conceptually I cannot figure out how to bond these tho very correlated features. Basically, we are not supposed to leave null data, however, this one is supposed to be null in numerical column by nature. If we make it "0", it has another meaning.
So, how should I pre-process this data to have the decision tree algorithm work properly?
scikit-learn numeric decision-tree categorical-data
asked Mar 21 at 16:58
BTurkeliBTurkeli
37118
add a comment |
I have a data including two columns where one is categorically shows the status of the feature & the other one numerically shows the related value. Just like below:
I want to run a decision tree algorithm via scikit learn on this data. I am not sure how to deal with these two columns because conceptually I cannot figure out how to bond these tho very correlated features. Basically, we are not supposed to leave null data, however, this one is supposed to be null in numerical column by nature. If we make it "0", it has another meaning.
So, how should I pre-process this data to have the decision tree algorithm work properly?
scikit-learn numeric decision-tree categorical-data
asked Mar 21 at 16:58
BTurkeliBTurkeli
37118
Please share what you have tried so far, and what specific programming issues you face; SO is not a code design service, I kindly suggest you re-read How to Ask and What topics can I ask about here?.
– desertnaut
Mar 21 at 18:03
Thanks for the insight.
– BTurkeli
Mar 22 at 5:04
add a comment |
I have a data including two columns where one is categorically shows the status of the feature & the other one numerically shows the related value. Just like below:
I want to run a decision tree algorithm via scikit learn on this data. I am not sure how to deal with these two columns because conceptually I cannot figure out how to bond these tho very correlated features. Basically, we are not supposed to leave null data, however, this one is supposed to be null in numerical column by nature. If we make it "0", it has another meaning.
So, how should I pre-process this data to have the decision tree algorithm work properly?
scikit-learn numeric decision-tree categorical-data
asked Mar 21 at 16:58
BTurkeliBTurkeli
37118
I have a data including two columns where one is categorically shows the status of the feature & the other one numerically shows the related value. Just like below:
I want to run a decision tree algorithm via scikit learn on this data. I am not sure how to deal with these two columns because conceptually I cannot figure out how to bond these tho very correlated features. Basically, we are not supposed to leave null data, however, this one is supposed to be null in numerical column by nature. If we make it "0", it has another meaning.
So, how should I pre-process this data to have the decision tree algorithm work properly?
scikit-learn numeric decision-tree categorical-data
scikit-learn numeric decision-tree categorical-data
asked Mar 21 at 16:58
BTurkeliBTurkeli
37118
asked Mar 21 at 16:58
BTurkeliBTurkeli
37118
asked Mar 21 at 16:58
BTurkeliBTurkeli
37118
asked Mar 21 at 16:58
BTurkeliBTurkeli
37118
asked Mar 21 at 16:58
BTurkeliBTurkeli
37118
37118
Please share what you have tried so far, and what specific programming issues you face; SO is not a code design service, I kindly suggest you re-read How to Ask and What topics can I ask about here?.
– desertnaut
Mar 21 at 18:03
Thanks for the insight.
– BTurkeli
Mar 22 at 5:04
add a comment |
Please share what you have tried so far, and what specific programming issues you face; SO is not a code design service, I kindly suggest you re-read How to Ask and What topics can I ask about here?.
– desertnaut
Mar 21 at 18:03
Thanks for the insight.
– BTurkeli
Mar 22 at 5:04
Please share what you have tried so far, and what specific programming issues you face; SO is not a code design service, I kindly suggest you re-read How to Ask and What topics can I ask about here?.
– desertnaut
Mar 21 at 18:03
Please share what you have tried so far, and what specific programming issues you face; SO is not a code design service, I kindly suggest you re-read How to Ask and What topics can I ask about here?.
– desertnaut
Mar 21 at 18:03
Thanks for the insight.
– BTurkeli
Mar 22 at 5:04
Thanks for the insight.
– BTurkeli
Mar 22 at 5:04
add a comment |
1 Answer
1
active
oldest
votes
My prefossor provides a reasonable answer as below.
First, fill the null cells with "0".
If you plug the data into decision tree algorithms with these two features, we have two cases:
If "Status" comes first:
The tree will split 0's and 1's into two branches. Under 0, all Amount values will be already 0, hence this feature will not be chosen. Under 1, there will not be any 0 Status.If "Amount" comes first: All Status 0's will go under only one branch and they will get together with the ones that are very small amounts.
So, If the Amount data is noisy, it might be helpful to keep the Status column. Otherwise, I would remove the Status column.
answered Mar 22 at 12:06
BTurkeliBTurkeli
37118
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
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oldest
votes
active
oldest
votes
My prefossor provides a reasonable answer as below.
First, fill the null cells with "0".
If you plug the data into decision tree algorithms with these two features, we have two cases:
If "Status" comes first:
The tree will split 0's and 1's into two branches. Under 0, all Amount values will be already 0, hence this feature will not be chosen. Under 1, there will not be any 0 Status.If "Amount" comes first: All Status 0's will go under only one branch and they will get together with the ones that are very small amounts.
So, If the Amount data is noisy, it might be helpful to keep the Status column. Otherwise, I would remove the Status column.
answered Mar 22 at 12:06
BTurkeliBTurkeli
37118
add a comment |
My prefossor provides a reasonable answer as below.
First, fill the null cells with "0".
If you plug the data into decision tree algorithms with these two features, we have two cases:
If "Status" comes first:
The tree will split 0's and 1's into two branches. Under 0, all Amount values will be already 0, hence this feature will not be chosen. Under 1, there will not be any 0 Status.If "Amount" comes first: All Status 0's will go under only one branch and they will get together with the ones that are very small amounts.
So, If the Amount data is noisy, it might be helpful to keep the Status column. Otherwise, I would remove the Status column.
answered Mar 22 at 12:06
BTurkeliBTurkeli
37118
add a comment |
My prefossor provides a reasonable answer as below.
First, fill the null cells with "0".
If you plug the data into decision tree algorithms with these two features, we have two cases:
If "Status" comes first:
The tree will split 0's and 1's into two branches. Under 0, all Amount values will be already 0, hence this feature will not be chosen. Under 1, there will not be any 0 Status.If "Amount" comes first: All Status 0's will go under only one branch and they will get together with the ones that are very small amounts.
So, If the Amount data is noisy, it might be helpful to keep the Status column. Otherwise, I would remove the Status column.
answered Mar 22 at 12:06
BTurkeliBTurkeli
37118
My prefossor provides a reasonable answer as below.
First, fill the null cells with "0".
If you plug the data into decision tree algorithms with these two features, we have two cases:
If "Status" comes first:
The tree will split 0's and 1's into two branches. Under 0, all Amount values will be already 0, hence this feature will not be chosen. Under 1, there will not be any 0 Status.If "Amount" comes first: All Status 0's will go under only one branch and they will get together with the ones that are very small amounts.
So, If the Amount data is noisy, it might be helpful to keep the Status column. Otherwise, I would remove the Status column.
answered Mar 22 at 12:06
BTurkeliBTurkeli
37118
answered Mar 22 at 12:06
BTurkeliBTurkeli
37118
answered Mar 22 at 12:06
BTurkeliBTurkeli
37118
answered Mar 22 at 12:06
BTurkeliBTurkeli
37118
37118
add a comment |
add a comment |
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Please share what you have tried so far, and what specific programming issues you face; SO is not a code design service, I kindly suggest you re-read How to Ask and What topics can I ask about here?.
– desertnaut
Mar 21 at 18:03
Thanks for the insight.
– BTurkeli
Mar 22 at 5:04