Which method should be considered to evaluate the imbalanced multi-class classification?
$begingroup$
I am working on multiclass-imbalanced data. My dependent variable is highly skewed.
Injury
2(No Injury) 208753
1(Medium Injury) 22318
0(severe Injury) 3394
I have used random forest algorithm with parameter "class_weight='balanced' " to manage the class 2 imbalance.
I get the below results when I use average='micro'.
[[ 34 107 688]
[ 148 778 4592]
[ 905 4635 46730]]
Accuracy Score: 0.8110616374089428
precision score: 0.8110616374089428
Recall score: 0.8110616374089428
AUC Score: 0.8582962280567071
F1 score: 0.8110616374089428
Kappa Score: 0.05522284663052324
For the average = 'macro', the results are below.
[[ 31 125 684]
[ 157 838 4559]
[ 890 4694 46639]]
Accuracy Score: 0.8104816009007626
precision score: 0.3586119227436326
Recall score: 0.3602869806251181
AUC Score: 0.5253225798824679
F1 score: 0.3592735337079687
Kappa Score: 0.06376296115668922
So, which results should I consider to evaluate the model? If I have to consider the macro, then my model performance is really bad. Please suggest if there are any methods to improve the precision, recall and AUC score?
If I consider micro results, my precision, recall, f1 score is same. How can I justify this in the project?
I am interested in medium and severe injuries. But individual class precisions 0.0312(severe), 0.1409(medium) for macro are very low. The overall precision score is also very low 0.35. Is it possible to increase these scores? Or Is it fine to consider these low values as my final results for the project? Any suggestions are greatly appreciated. NOTE: I have tried SMOTE oversampling and ensemble cross-validation with different algorithms but I ended up having precision and recall scores less than 50.
Any help would be appreciated.
Thank you.
machine-learning neural-network deep-learning multiclass-classification class-imbalance
asked Nov 7 '18 at 9:30
Bhaskar SharmaBhaskar Sharma
183
$endgroup$
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I am working on multiclass-imbalanced data. My dependent variable is highly skewed.
Injury
2(No Injury) 208753
1(Medium Injury) 22318
0(severe Injury) 3394
I have used random forest algorithm with parameter "class_weight='balanced' " to manage the class 2 imbalance.
I get the below results when I use average='micro'.
[[ 34 107 688]
[ 148 778 4592]
[ 905 4635 46730]]
Accuracy Score: 0.8110616374089428
precision score: 0.8110616374089428
Recall score: 0.8110616374089428
AUC Score: 0.8582962280567071
F1 score: 0.8110616374089428
Kappa Score: 0.05522284663052324
For the average = 'macro', the results are below.
[[ 31 125 684]
[ 157 838 4559]
[ 890 4694 46639]]
Accuracy Score: 0.8104816009007626
precision score: 0.3586119227436326
Recall score: 0.3602869806251181
AUC Score: 0.5253225798824679
F1 score: 0.3592735337079687
Kappa Score: 0.06376296115668922
So, which results should I consider to evaluate the model? If I have to consider the macro, then my model performance is really bad. Please suggest if there are any methods to improve the precision, recall and AUC score?
If I consider micro results, my precision, recall, f1 score is same. How can I justify this in the project?
I am interested in medium and severe injuries. But individual class precisions 0.0312(severe), 0.1409(medium) for macro are very low. The overall precision score is also very low 0.35. Is it possible to increase these scores? Or Is it fine to consider these low values as my final results for the project? Any suggestions are greatly appreciated. NOTE: I have tried SMOTE oversampling and ensemble cross-validation with different algorithms but I ended up having precision and recall scores less than 50.
Any help would be appreciated.
Thank you.
machine-learning neural-network deep-learning multiclass-classification class-imbalance
asked Nov 7 '18 at 9:30
Bhaskar SharmaBhaskar Sharma
183
$endgroup$
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
If you think that there is a problem to your model by the imbalanceness of the classes, have you considered using a resampling methods such SMOTE for oversappling?
$endgroup$
– JoPapou13
Nov 7 '18 at 19:45
$begingroup$
Yes, I have used smote method to oversample and then computed the above results.
$endgroup$
– Bhaskar Sharma
Nov 7 '18 at 20:00
add a comment |
$begingroup$
I am working on multiclass-imbalanced data. My dependent variable is highly skewed.
Injury
2(No Injury) 208753
1(Medium Injury) 22318
0(severe Injury) 3394
I have used random forest algorithm with parameter "class_weight='balanced' " to manage the class 2 imbalance.
I get the below results when I use average='micro'.
[[ 34 107 688]
[ 148 778 4592]
[ 905 4635 46730]]
Accuracy Score: 0.8110616374089428
precision score: 0.8110616374089428
Recall score: 0.8110616374089428
AUC Score: 0.8582962280567071
F1 score: 0.8110616374089428
Kappa Score: 0.05522284663052324
For the average = 'macro', the results are below.
[[ 31 125 684]
[ 157 838 4559]
[ 890 4694 46639]]
Accuracy Score: 0.8104816009007626
precision score: 0.3586119227436326
Recall score: 0.3602869806251181
AUC Score: 0.5253225798824679
F1 score: 0.3592735337079687
Kappa Score: 0.06376296115668922
So, which results should I consider to evaluate the model? If I have to consider the macro, then my model performance is really bad. Please suggest if there are any methods to improve the precision, recall and AUC score?
If I consider micro results, my precision, recall, f1 score is same. How can I justify this in the project?
I am interested in medium and severe injuries. But individual class precisions 0.0312(severe), 0.1409(medium) for macro are very low. The overall precision score is also very low 0.35. Is it possible to increase these scores? Or Is it fine to consider these low values as my final results for the project? Any suggestions are greatly appreciated. NOTE: I have tried SMOTE oversampling and ensemble cross-validation with different algorithms but I ended up having precision and recall scores less than 50.
Any help would be appreciated.
Thank you.
machine-learning neural-network deep-learning multiclass-classification class-imbalance
asked Nov 7 '18 at 9:30
Bhaskar SharmaBhaskar Sharma
183
$endgroup$
I am working on multiclass-imbalanced data. My dependent variable is highly skewed.
Injury
2(No Injury) 208753
1(Medium Injury) 22318
0(severe Injury) 3394
I have used random forest algorithm with parameter "class_weight='balanced' " to manage the class 2 imbalance.
I get the below results when I use average='micro'.
[[ 34 107 688]
[ 148 778 4592]
[ 905 4635 46730]]
Accuracy Score: 0.8110616374089428
precision score: 0.8110616374089428
Recall score: 0.8110616374089428
AUC Score: 0.8582962280567071
F1 score: 0.8110616374089428
Kappa Score: 0.05522284663052324
For the average = 'macro', the results are below.
[[ 31 125 684]
[ 157 838 4559]
[ 890 4694 46639]]
Accuracy Score: 0.8104816009007626
precision score: 0.3586119227436326
Recall score: 0.3602869806251181
AUC Score: 0.5253225798824679
F1 score: 0.3592735337079687
Kappa Score: 0.06376296115668922
So, which results should I consider to evaluate the model? If I have to consider the macro, then my model performance is really bad. Please suggest if there are any methods to improve the precision, recall and AUC score?
If I consider micro results, my precision, recall, f1 score is same. How can I justify this in the project?
I am interested in medium and severe injuries. But individual class precisions 0.0312(severe), 0.1409(medium) for macro are very low. The overall precision score is also very low 0.35. Is it possible to increase these scores? Or Is it fine to consider these low values as my final results for the project? Any suggestions are greatly appreciated. NOTE: I have tried SMOTE oversampling and ensemble cross-validation with different algorithms but I ended up having precision and recall scores less than 50.
Any help would be appreciated.
Thank you.
machine-learning neural-network deep-learning multiclass-classification class-imbalance
machine-learning neural-network deep-learning multiclass-classification class-imbalance
asked Nov 7 '18 at 9:30
Bhaskar SharmaBhaskar Sharma
183
asked Nov 7 '18 at 9:30
Bhaskar SharmaBhaskar Sharma
183
edited Nov 8 '18 at 8:37
Bhaskar Sharma
asked Nov 7 '18 at 9:30
Bhaskar SharmaBhaskar Sharma
183
asked Nov 7 '18 at 9:30
Bhaskar SharmaBhaskar Sharma
183
asked Nov 7 '18 at 9:30
Bhaskar SharmaBhaskar Sharma
183
183
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
If you think that there is a problem to your model by the imbalanceness of the classes, have you considered using a resampling methods such SMOTE for oversappling?
$endgroup$
– JoPapou13
Nov 7 '18 at 19:45
$begingroup$
Yes, I have used smote method to oversample and then computed the above results.
$endgroup$
– Bhaskar Sharma
Nov 7 '18 at 20:00
add a comment |
$begingroup$
If you think that there is a problem to your model by the imbalanceness of the classes, have you considered using a resampling methods such SMOTE for oversappling?
$endgroup$
– JoPapou13
Nov 7 '18 at 19:45
$begingroup$
Yes, I have used smote method to oversample and then computed the above results.
$endgroup$
– Bhaskar Sharma
Nov 7 '18 at 20:00
$begingroup$
If you think that there is a problem to your model by the imbalanceness of the classes, have you considered using a resampling methods such SMOTE for oversappling?
$endgroup$
– JoPapou13
Nov 7 '18 at 19:45
$begingroup$
If you think that there is a problem to your model by the imbalanceness of the classes, have you considered using a resampling methods such SMOTE for oversappling?
$endgroup$
– JoPapou13
Nov 7 '18 at 19:45
$begingroup$
Yes, I have used smote method to oversample and then computed the above results.
$endgroup$
– Bhaskar Sharma
Nov 7 '18 at 20:00
$begingroup$
Yes, I have used smote method to oversample and then computed the above results.
$endgroup$
– Bhaskar Sharma
Nov 7 '18 at 20:00
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
For imbalanced datasets you can employ F1 score. It considers both rare and common classes. You can take a look at this article if you are not familiar with that.
answered Nov 7 '18 at 20:01
VaalizaadehVaalizaadeh
7,55062263
$endgroup$
add a comment |
$begingroup$
This answer explains how micro and macro are different and which one you should use.
answered Nov 7 '18 at 17:46
Gyan RanjanGyan Ranjan
3508
$endgroup$
add a comment |
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2 Answers
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2 Answers
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$begingroup$
For imbalanced datasets you can employ F1 score. It considers both rare and common classes. You can take a look at this article if you are not familiar with that.
answered Nov 7 '18 at 20:01
VaalizaadehVaalizaadeh
7,55062263
$endgroup$
add a comment |
$begingroup$
For imbalanced datasets you can employ F1 score. It considers both rare and common classes. You can take a look at this article if you are not familiar with that.
answered Nov 7 '18 at 20:01
VaalizaadehVaalizaadeh
7,55062263
$endgroup$
add a comment |
$begingroup$
For imbalanced datasets you can employ F1 score. It considers both rare and common classes. You can take a look at this article if you are not familiar with that.
answered Nov 7 '18 at 20:01
VaalizaadehVaalizaadeh
7,55062263
$endgroup$
For imbalanced datasets you can employ F1 score. It considers both rare and common classes. You can take a look at this article if you are not familiar with that.
answered Nov 7 '18 at 20:01
VaalizaadehVaalizaadeh
7,55062263
answered Nov 7 '18 at 20:01
VaalizaadehVaalizaadeh
7,55062263
answered Nov 7 '18 at 20:01
VaalizaadehVaalizaadeh
7,55062263
answered Nov 7 '18 at 20:01
VaalizaadehVaalizaadeh
7,55062263
7,55062263
add a comment |
add a comment |
$begingroup$
This answer explains how micro and macro are different and which one you should use.
answered Nov 7 '18 at 17:46
Gyan RanjanGyan Ranjan
3508
$endgroup$
add a comment |
$begingroup$
This answer explains how micro and macro are different and which one you should use.
answered Nov 7 '18 at 17:46
Gyan RanjanGyan Ranjan
3508
$endgroup$
add a comment |
$begingroup$
This answer explains how micro and macro are different and which one you should use.
answered Nov 7 '18 at 17:46
Gyan RanjanGyan Ranjan
3508
$endgroup$
This answer explains how micro and macro are different and which one you should use.
answered Nov 7 '18 at 17:46
Gyan RanjanGyan Ranjan
3508
edited Nov 8 '18 at 1:47
answered Nov 7 '18 at 17:46
Gyan RanjanGyan Ranjan
3508
answered Nov 7 '18 at 17:46
Gyan RanjanGyan Ranjan
3508
answered Nov 7 '18 at 17:46
Gyan RanjanGyan Ranjan
3508
3508
add a comment |
add a comment |
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$begingroup$
If you think that there is a problem to your model by the imbalanceness of the classes, have you considered using a resampling methods such SMOTE for oversappling?
$endgroup$
– JoPapou13
Nov 7 '18 at 19:45
$begingroup$
Yes, I have used smote method to oversample and then computed the above results.
$endgroup$
– Bhaskar Sharma
Nov 7 '18 at 20:00