Including identifier in machine learning model as feature vs separate model for every identifier
$begingroup$
I am new to machine learning and i am building a model to predict number of customers for the model branch at specific hour/season/other feature.
I know it will be bad idea to pit id(branch_id in my case) into model but customer count in this case hugely depend on which branch it is so i cannot exclude it.
I can think of two solutions, i am not sure which one is right and what is the best practice.
- Create dummy variable(one hot encoding to avoid wieghing one id more than other) for all branch ids,but since i have 600 unique branch ids my features will go up-to
600+rest_of_features. - Learn a separate model for each of the branch(600 models), i am not sure if it is right approach and also i am not very familiar with this approach and it will be very time consuming.
Looking for the suggestion
Example of the data is below
+-----------+------+-----------+-----------+-------------------+
| branch_id | hour | feature_2 | feature_3 | Count of customer |
+-----------+------+-----------+-----------+-------------------+
| 1 | 12 | .. | .. | 19 |
| 1 | 01 | .. | .. | 25 |
| 2 | 23 | .. | .. | 14 |
| 2 | 01 | .. | .. | 5 |
+-----------+------+-----------+-----------+-------------------+
machine-learning feature-selection
asked 17 mins ago
mashrafmashraf
1
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add a comment |
$begingroup$
I am new to machine learning and i am building a model to predict number of customers for the model branch at specific hour/season/other feature.
I know it will be bad idea to pit id(branch_id in my case) into model but customer count in this case hugely depend on which branch it is so i cannot exclude it.
I can think of two solutions, i am not sure which one is right and what is the best practice.
- Create dummy variable(one hot encoding to avoid wieghing one id more than other) for all branch ids,but since i have 600 unique branch ids my features will go up-to
600+rest_of_features. - Learn a separate model for each of the branch(600 models), i am not sure if it is right approach and also i am not very familiar with this approach and it will be very time consuming.
Looking for the suggestion
Example of the data is below
+-----------+------+-----------+-----------+-------------------+
| branch_id | hour | feature_2 | feature_3 | Count of customer |
+-----------+------+-----------+-----------+-------------------+
| 1 | 12 | .. | .. | 19 |
| 1 | 01 | .. | .. | 25 |
| 2 | 23 | .. | .. | 14 |
| 2 | 01 | .. | .. | 5 |
+-----------+------+-----------+-----------+-------------------+
machine-learning feature-selection
asked 17 mins ago
mashrafmashraf
1
New contributor
mashraf 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 new to machine learning and i am building a model to predict number of customers for the model branch at specific hour/season/other feature.
I know it will be bad idea to pit id(branch_id in my case) into model but customer count in this case hugely depend on which branch it is so i cannot exclude it.
I can think of two solutions, i am not sure which one is right and what is the best practice.
- Create dummy variable(one hot encoding to avoid wieghing one id more than other) for all branch ids,but since i have 600 unique branch ids my features will go up-to
600+rest_of_features. - Learn a separate model for each of the branch(600 models), i am not sure if it is right approach and also i am not very familiar with this approach and it will be very time consuming.
Looking for the suggestion
Example of the data is below
+-----------+------+-----------+-----------+-------------------+
| branch_id | hour | feature_2 | feature_3 | Count of customer |
+-----------+------+-----------+-----------+-------------------+
| 1 | 12 | .. | .. | 19 |
| 1 | 01 | .. | .. | 25 |
| 2 | 23 | .. | .. | 14 |
| 2 | 01 | .. | .. | 5 |
+-----------+------+-----------+-----------+-------------------+
machine-learning feature-selection
asked 17 mins ago
mashrafmashraf
1
New contributor
mashraf 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 new to machine learning and i am building a model to predict number of customers for the model branch at specific hour/season/other feature.
I know it will be bad idea to pit id(branch_id in my case) into model but customer count in this case hugely depend on which branch it is so i cannot exclude it.
I can think of two solutions, i am not sure which one is right and what is the best practice.
- Create dummy variable(one hot encoding to avoid wieghing one id more than other) for all branch ids,but since i have 600 unique branch ids my features will go up-to
600+rest_of_features. - Learn a separate model for each of the branch(600 models), i am not sure if it is right approach and also i am not very familiar with this approach and it will be very time consuming.
Looking for the suggestion
Example of the data is below
+-----------+------+-----------+-----------+-------------------+
| branch_id | hour | feature_2 | feature_3 | Count of customer |
+-----------+------+-----------+-----------+-------------------+
| 1 | 12 | .. | .. | 19 |
| 1 | 01 | .. | .. | 25 |
| 2 | 23 | .. | .. | 14 |
| 2 | 01 | .. | .. | 5 |
+-----------+------+-----------+-----------+-------------------+
machine-learning feature-selection
machine-learning feature-selection
asked 17 mins ago
mashrafmashraf
1
New contributor
mashraf is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 17 mins ago
mashrafmashraf
1
New contributor
mashraf is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 17 mins ago
mashrafmashraf
1
New contributor
mashraf is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 17 mins ago
mashrafmashraf
1
asked 17 mins ago
mashrafmashraf
1
1
New contributor
mashraf is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
mashraf 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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Check out our Code of Conduct.
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1 Answer
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In my opinion including id as a feature will not make sense at all, because the model will treat the id as a numeric value which will decrease the model performance, because there should be no connection how big the id is and how many customers there are for that id.
Option 2 can make sense if you have enough data for every branch.
My suggestion will be to look deep into your features and try to find a feature which will replace the branch id. Let's say the number of supporting desks in a branch or the location of a branch as a categorical value. If you find enough features that can describe the specifics of branches, then no need to include ids or to do it separately.
answered 2 mins ago
Karen DanielyanKaren Danielyan
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1 Answer
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$begingroup$
In my opinion including id as a feature will not make sense at all, because the model will treat the id as a numeric value which will decrease the model performance, because there should be no connection how big the id is and how many customers there are for that id.
Option 2 can make sense if you have enough data for every branch.
My suggestion will be to look deep into your features and try to find a feature which will replace the branch id. Let's say the number of supporting desks in a branch or the location of a branch as a categorical value. If you find enough features that can describe the specifics of branches, then no need to include ids or to do it separately.
answered 2 mins ago
Karen DanielyanKaren Danielyan
1
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Karen Danielyan 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 |
$begingroup$
In my opinion including id as a feature will not make sense at all, because the model will treat the id as a numeric value which will decrease the model performance, because there should be no connection how big the id is and how many customers there are for that id.
Option 2 can make sense if you have enough data for every branch.
My suggestion will be to look deep into your features and try to find a feature which will replace the branch id. Let's say the number of supporting desks in a branch or the location of a branch as a categorical value. If you find enough features that can describe the specifics of branches, then no need to include ids or to do it separately.
answered 2 mins ago
Karen DanielyanKaren Danielyan
1
New contributor
Karen Danielyan 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 |
$begingroup$
In my opinion including id as a feature will not make sense at all, because the model will treat the id as a numeric value which will decrease the model performance, because there should be no connection how big the id is and how many customers there are for that id.
Option 2 can make sense if you have enough data for every branch.
My suggestion will be to look deep into your features and try to find a feature which will replace the branch id. Let's say the number of supporting desks in a branch or the location of a branch as a categorical value. If you find enough features that can describe the specifics of branches, then no need to include ids or to do it separately.
answered 2 mins ago
Karen DanielyanKaren Danielyan
1
New contributor
Karen Danielyan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
In my opinion including id as a feature will not make sense at all, because the model will treat the id as a numeric value which will decrease the model performance, because there should be no connection how big the id is and how many customers there are for that id.
Option 2 can make sense if you have enough data for every branch.
My suggestion will be to look deep into your features and try to find a feature which will replace the branch id. Let's say the number of supporting desks in a branch or the location of a branch as a categorical value. If you find enough features that can describe the specifics of branches, then no need to include ids or to do it separately.
answered 2 mins ago
Karen DanielyanKaren Danielyan
1
New contributor
Karen Danielyan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 2 mins ago
Karen DanielyanKaren Danielyan
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answered 2 mins ago
Karen DanielyanKaren Danielyan
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answered 2 mins ago
Karen DanielyanKaren Danielyan
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