Multi class logistic regression performs bad on certain classes
$begingroup$
I'm trying to predict the day of the week for customers next visit from their previous visits (0 is they won't visit, 1 is Monday and so on). I have created some features like the visits days ratios, average duration between visits..etc.
My model performs really bad and i'm trying to diagnose it. I have used the classification report. How can interpret that some classes are completely misclassified?
precision recall f1-score support
0 0.45 0.98 0.62 26791
1 0.00 0.00 0.00 3771
2 0.00 0.00 0.00 2702
3 0.00 0.00 0.00 2744
4 0.00 0.00 0.00 2504
5 0.00 0.00 0.00 2735
6 0.35 0.06 0.10 9988
7 0.35 0.00 0.00 8765
avg / total 0.31 0.45 0.29 60000
logistic-regression performance
asked Apr 24 '18 at 17:50
Iyad Al aqelIyad Al aqel
1111
$endgroup$
add a comment |
$begingroup$
I'm trying to predict the day of the week for customers next visit from their previous visits (0 is they won't visit, 1 is Monday and so on). I have created some features like the visits days ratios, average duration between visits..etc.
My model performs really bad and i'm trying to diagnose it. I have used the classification report. How can interpret that some classes are completely misclassified?
precision recall f1-score support
0 0.45 0.98 0.62 26791
1 0.00 0.00 0.00 3771
2 0.00 0.00 0.00 2702
3 0.00 0.00 0.00 2744
4 0.00 0.00 0.00 2504
5 0.00 0.00 0.00 2735
6 0.35 0.06 0.10 9988
7 0.35 0.00 0.00 8765
avg / total 0.31 0.45 0.29 60000
logistic-regression performance
asked Apr 24 '18 at 17:50
Iyad Al aqelIyad Al aqel
1111
$endgroup$
$begingroup$
your features don't sound very predictive for the weekday of the visit... try predicting any visit (combining 1-7) and create additional features
$endgroup$
– oW_
Apr 24 '18 at 17:56
$begingroup$
If the goal is to predict the day not (any visit). How would i go from predicting any visit to predicting the day?
$endgroup$
– Iyad Al aqel
Apr 24 '18 at 18:28
add a comment |
$begingroup$
I'm trying to predict the day of the week for customers next visit from their previous visits (0 is they won't visit, 1 is Monday and so on). I have created some features like the visits days ratios, average duration between visits..etc.
My model performs really bad and i'm trying to diagnose it. I have used the classification report. How can interpret that some classes are completely misclassified?
precision recall f1-score support
0 0.45 0.98 0.62 26791
1 0.00 0.00 0.00 3771
2 0.00 0.00 0.00 2702
3 0.00 0.00 0.00 2744
4 0.00 0.00 0.00 2504
5 0.00 0.00 0.00 2735
6 0.35 0.06 0.10 9988
7 0.35 0.00 0.00 8765
avg / total 0.31 0.45 0.29 60000
logistic-regression performance
asked Apr 24 '18 at 17:50
Iyad Al aqelIyad Al aqel
1111
$endgroup$
I'm trying to predict the day of the week for customers next visit from their previous visits (0 is they won't visit, 1 is Monday and so on). I have created some features like the visits days ratios, average duration between visits..etc.
My model performs really bad and i'm trying to diagnose it. I have used the classification report. How can interpret that some classes are completely misclassified?
precision recall f1-score support
0 0.45 0.98 0.62 26791
1 0.00 0.00 0.00 3771
2 0.00 0.00 0.00 2702
3 0.00 0.00 0.00 2744
4 0.00 0.00 0.00 2504
5 0.00 0.00 0.00 2735
6 0.35 0.06 0.10 9988
7 0.35 0.00 0.00 8765
avg / total 0.31 0.45 0.29 60000
logistic-regression performance
logistic-regression performance
asked Apr 24 '18 at 17:50
Iyad Al aqelIyad Al aqel
1111
asked Apr 24 '18 at 17:50
Iyad Al aqelIyad Al aqel
1111
asked Apr 24 '18 at 17:50
Iyad Al aqelIyad Al aqel
1111
asked Apr 24 '18 at 17:50
Iyad Al aqelIyad Al aqel
1111
asked Apr 24 '18 at 17:50
Iyad Al aqelIyad Al aqel
1111
1111
$begingroup$
your features don't sound very predictive for the weekday of the visit... try predicting any visit (combining 1-7) and create additional features
$endgroup$
– oW_
Apr 24 '18 at 17:56
$begingroup$
If the goal is to predict the day not (any visit). How would i go from predicting any visit to predicting the day?
$endgroup$
– Iyad Al aqel
Apr 24 '18 at 18:28
add a comment |
$begingroup$
your features don't sound very predictive for the weekday of the visit... try predicting any visit (combining 1-7) and create additional features
$endgroup$
– oW_
Apr 24 '18 at 17:56
$begingroup$
If the goal is to predict the day not (any visit). How would i go from predicting any visit to predicting the day?
$endgroup$
– Iyad Al aqel
Apr 24 '18 at 18:28
$begingroup$
your features don't sound very predictive for the weekday of the visit... try predicting any visit (combining 1-7) and create additional features
$endgroup$
– oW_
Apr 24 '18 at 17:56
$begingroup$
your features don't sound very predictive for the weekday of the visit... try predicting any visit (combining 1-7) and create additional features
$endgroup$
– oW_
Apr 24 '18 at 17:56
$begingroup$
If the goal is to predict the day not (any visit). How would i go from predicting any visit to predicting the day?
$endgroup$
– Iyad Al aqel
Apr 24 '18 at 18:28
$begingroup$
If the goal is to predict the day not (any visit). How would i go from predicting any visit to predicting the day?
$endgroup$
– Iyad Al aqel
Apr 24 '18 at 18:28
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
You are not accounting for multiple visits within the same week. Say you have a customer visiting on a Monday and Wednesday of the same week, what will you have in your training set.
Multiple visits per week. I'm not sure if you have done some feature engineering like below along with visit ratios, frequency, and average duration per visit.
| customer_id | week | visit_day |
| 1 | 1 | 1 |
| 1 | 1 | 3 |
To account for non-visits, you need to have rows to mention 0 visits.
A couple of ways to look at the problem -
One, you have a customer and week level dataset and train a multinomial classifier.
Two, combine the results of eight binary classifiers, you can weight the proportions of the visit on a particular day to the probability of the predicted visit day.
Since you are doing extensive feature engineering here, I suggest a tree-based approach for your classifier(s).
answered 18 hours ago
Next Door EngineerNext Door Engineer
1011
New contributor
Next Door Engineer 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
1
active
oldest
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1 Answer
1
active
oldest
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active
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$begingroup$
You are not accounting for multiple visits within the same week. Say you have a customer visiting on a Monday and Wednesday of the same week, what will you have in your training set.
Multiple visits per week. I'm not sure if you have done some feature engineering like below along with visit ratios, frequency, and average duration per visit.
| customer_id | week | visit_day |
| 1 | 1 | 1 |
| 1 | 1 | 3 |
To account for non-visits, you need to have rows to mention 0 visits.
A couple of ways to look at the problem -
One, you have a customer and week level dataset and train a multinomial classifier.
Two, combine the results of eight binary classifiers, you can weight the proportions of the visit on a particular day to the probability of the predicted visit day.
Since you are doing extensive feature engineering here, I suggest a tree-based approach for your classifier(s).
answered 18 hours ago
Next Door EngineerNext Door Engineer
1011
New contributor
Next Door Engineer 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 |
$begingroup$
You are not accounting for multiple visits within the same week. Say you have a customer visiting on a Monday and Wednesday of the same week, what will you have in your training set.
Multiple visits per week. I'm not sure if you have done some feature engineering like below along with visit ratios, frequency, and average duration per visit.
| customer_id | week | visit_day |
| 1 | 1 | 1 |
| 1 | 1 | 3 |
To account for non-visits, you need to have rows to mention 0 visits.
A couple of ways to look at the problem -
One, you have a customer and week level dataset and train a multinomial classifier.
Two, combine the results of eight binary classifiers, you can weight the proportions of the visit on a particular day to the probability of the predicted visit day.
Since you are doing extensive feature engineering here, I suggest a tree-based approach for your classifier(s).
answered 18 hours ago
Next Door EngineerNext Door Engineer
1011
New contributor
Next Door Engineer 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 |
$begingroup$
You are not accounting for multiple visits within the same week. Say you have a customer visiting on a Monday and Wednesday of the same week, what will you have in your training set.
Multiple visits per week. I'm not sure if you have done some feature engineering like below along with visit ratios, frequency, and average duration per visit.
| customer_id | week | visit_day |
| 1 | 1 | 1 |
| 1 | 1 | 3 |
To account for non-visits, you need to have rows to mention 0 visits.
A couple of ways to look at the problem -
One, you have a customer and week level dataset and train a multinomial classifier.
Two, combine the results of eight binary classifiers, you can weight the proportions of the visit on a particular day to the probability of the predicted visit day.
Since you are doing extensive feature engineering here, I suggest a tree-based approach for your classifier(s).
answered 18 hours ago
Next Door EngineerNext Door Engineer
1011
New contributor
Next Door Engineer is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
You are not accounting for multiple visits within the same week. Say you have a customer visiting on a Monday and Wednesday of the same week, what will you have in your training set.
Multiple visits per week. I'm not sure if you have done some feature engineering like below along with visit ratios, frequency, and average duration per visit.
| customer_id | week | visit_day |
| 1 | 1 | 1 |
| 1 | 1 | 3 |
To account for non-visits, you need to have rows to mention 0 visits.
A couple of ways to look at the problem -
One, you have a customer and week level dataset and train a multinomial classifier.
Two, combine the results of eight binary classifiers, you can weight the proportions of the visit on a particular day to the probability of the predicted visit day.
Since you are doing extensive feature engineering here, I suggest a tree-based approach for your classifier(s).
answered 18 hours ago
Next Door EngineerNext Door Engineer
1011
New contributor
Next Door Engineer is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 18 hours ago
Next Door EngineerNext Door Engineer
1011
New contributor
Next Door Engineer is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 18 hours ago
Next Door EngineerNext Door Engineer
1011
answered 18 hours ago
Next Door EngineerNext Door Engineer
1011
1011
New contributor
Next Door Engineer is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Next Door Engineer is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Next Door Engineer is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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$begingroup$
your features don't sound very predictive for the weekday of the visit... try predicting any visit (combining 1-7) and create additional features
$endgroup$
– oW_
Apr 24 '18 at 17:56
$begingroup$
If the goal is to predict the day not (any visit). How would i go from predicting any visit to predicting the day?
$endgroup$
– Iyad Al aqel
Apr 24 '18 at 18:28