What should be done to handle Imbalanced classes in case of Multi-class classification
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I have a dataset which consist of user tickets which is random in pattern and some 56 cols in it and it is a text data. My task is to create a model and train it to identify and predict to which category the tickets belongs to, and we have 100 + category being there. The count for suppose category A is 70,000 other is 50,0000 and for some category the ticket count goes down to 1 is this an imbalanced data? If is is how should i handle this for multi class classification and for till now to handle this data which i think is is imbalanced i am using SMOTE but the accuracy decreases. What should i do in this case?
I have already tried DecisionTree classifier and now working on Logisitic regression.
machine-learning data-analysis
asked Jan 4 at 12:00
pratha1995pratha1995
5510
add a comment |
I have a dataset which consist of user tickets which is random in pattern and some 56 cols in it and it is a text data. My task is to create a model and train it to identify and predict to which category the tickets belongs to, and we have 100 + category being there. The count for suppose category A is 70,000 other is 50,0000 and for some category the ticket count goes down to 1 is this an imbalanced data? If is is how should i handle this for multi class classification and for till now to handle this data which i think is is imbalanced i am using SMOTE but the accuracy decreases. What should i do in this case?
I have already tried DecisionTree classifier and now working on Logisitic regression.
machine-learning data-analysis
asked Jan 4 at 12:00
pratha1995pratha1995
5510
add a comment |
I have a dataset which consist of user tickets which is random in pattern and some 56 cols in it and it is a text data. My task is to create a model and train it to identify and predict to which category the tickets belongs to, and we have 100 + category being there. The count for suppose category A is 70,000 other is 50,0000 and for some category the ticket count goes down to 1 is this an imbalanced data? If is is how should i handle this for multi class classification and for till now to handle this data which i think is is imbalanced i am using SMOTE but the accuracy decreases. What should i do in this case?
I have already tried DecisionTree classifier and now working on Logisitic regression.
machine-learning data-analysis
asked Jan 4 at 12:00
pratha1995pratha1995
5510
I have a dataset which consist of user tickets which is random in pattern and some 56 cols in it and it is a text data. My task is to create a model and train it to identify and predict to which category the tickets belongs to, and we have 100 + category being there. The count for suppose category A is 70,000 other is 50,0000 and for some category the ticket count goes down to 1 is this an imbalanced data? If is is how should i handle this for multi class classification and for till now to handle this data which i think is is imbalanced i am using SMOTE but the accuracy decreases. What should i do in this case?
I have already tried DecisionTree classifier and now working on Logisitic regression.
machine-learning data-analysis
machine-learning data-analysis
asked Jan 4 at 12:00
pratha1995pratha1995
5510
asked Jan 4 at 12:00
pratha1995pratha1995
5510
asked Jan 4 at 12:00
pratha1995pratha1995
5510
asked Jan 4 at 12:00
pratha1995pratha1995
5510
asked Jan 4 at 12:00
pratha1995pratha1995
5510
5510
add a comment |
add a comment |
1 Answer
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oldest
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1) Use F1-score as the evaluation metric in such cases(highly imbalanced data).
2) Use stratified sampling while train_test split.
3) Try one vs rest classifier.
4) Use algorithms like xgboost, lightgbm and catboost.
answered Jan 4 at 12:18
RavikiranRavikiran
1217
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
1) Use F1-score as the evaluation metric in such cases(highly imbalanced data).
2) Use stratified sampling while train_test split.
3) Try one vs rest classifier.
4) Use algorithms like xgboost, lightgbm and catboost.
answered Jan 4 at 12:18
RavikiranRavikiran
1217
add a comment |
1) Use F1-score as the evaluation metric in such cases(highly imbalanced data).
2) Use stratified sampling while train_test split.
3) Try one vs rest classifier.
4) Use algorithms like xgboost, lightgbm and catboost.
answered Jan 4 at 12:18
RavikiranRavikiran
1217
add a comment |
1) Use F1-score as the evaluation metric in such cases(highly imbalanced data).
2) Use stratified sampling while train_test split.
3) Try one vs rest classifier.
4) Use algorithms like xgboost, lightgbm and catboost.
answered Jan 4 at 12:18
RavikiranRavikiran
1217
1) Use F1-score as the evaluation metric in such cases(highly imbalanced data).
2) Use stratified sampling while train_test split.
3) Try one vs rest classifier.
4) Use algorithms like xgboost, lightgbm and catboost.
answered Jan 4 at 12:18
RavikiranRavikiran
1217
answered Jan 4 at 12:18
RavikiranRavikiran
1217
answered Jan 4 at 12:18
RavikiranRavikiran
1217
answered Jan 4 at 12:18
RavikiranRavikiran
1217
1217
add a comment |
add a comment |
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