I am new in data science I want to handle imbalance data how do i do that?
Try SMOTE.
i tried smote but result is so bad.
I tried SMOTE and didn't work !!!
On the anomaly detection w/ autoenconder front i am looking at this document : https://www.cs.ru.nl/bachelors-theses/2018/Tom_Sweers___4584325___Autoencoding_credit_card_fraude.pdf
I am trying just for kicks because i think this is a very signal poor dataset.
The best way to handle such an instance is to try Downsampling or Upsampling.
Please check this site and blog out..
http://www.simafore.com/blog/handling-unbalanced-data-machine-learning-models
https://towardsdatascience.com/methods-for-dealing-with-imbalanced-data-5b761be45a18
Hi jayesh! There are quite a number of ways to deal with imbalanced datasets. Some of these are:
1. Use of SMOTE
2. Use of the NearMiss algorithm
3. Random Sampling (Undersampling and/or Oversampling)
4. Anomaly Detection
5. Use of certain ensemble learning models.
Since the results you obtained on using SMOTE gave you low scores, I will advise you to check your features very well...something may be fishy.
Try SMOTE.
i tried smote but result is so bad.
I tried SMOTE and didn't work !!!
On the anomaly detection w/ autoenconder front i am looking at this document : https://www.cs.ru.nl/bachelors-theses/2018/Tom_Sweers___4584325___Autoencoding_credit_card_fraude.pdf
I am trying just for kicks because i think this is a very signal poor dataset.
The best way to handle such an instance is to try Downsampling or Upsampling.
Please check this site and blog out..
http://www.simafore.com/blog/handling-unbalanced-data-machine-learning-models
https://towardsdatascience.com/methods-for-dealing-with-imbalanced-data-5b761be45a18
Hi jayesh! There are quite a number of ways to deal with imbalanced datasets. Some of these are:
1. Use of SMOTE
2. Use of the NearMiss algorithm
3. Random Sampling (Undersampling and/or Oversampling)
4. Anomaly Detection
5. Use of certain ensemble learning models.
Since the results you obtained on using SMOTE gave you low scores, I will advise you to check your features very well...something may be fishy.