Questions tagged [smote]

Synthetic Minority Oversampling Technique (SMOTE) is an approach used for dealing with imbalanced datasets before running them through machine learning models.

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7
votes
1answer
744 views

Why you shouldn't upsample before cross validation

I have an imbalanced dataset and I am trying different methods to address the data imbalance. I found this article that explains the correct way to cross validate when oversampling data using SMOTE ...
20
votes
3answers
19k views

How do you apply SMOTE on text classification?

Synthetic Minority Oversampling Technique (SMOTE) is an oversampling technique used in an imbalanced dataset problem. So far I have an idea how to apply it on generic, structured data. But is it ...
15
votes
3answers
15k views

Train/Test Split after performing SMOTE

I am dealing with a highly unbalanced dataset so I used SMOTE to resample it. After SMOTE resampling, I split the resampled dataset into training/test sets using the training set to build a model and ...
1
vote
1answer
1k views

Scripting code for class imbalance in Biolabs Orange

I'm trying to manipulate some data in Biolabs Orange, using the built in Python Script widget and information at Biolabs Orange tutorial on scripting. However, I'm struggling with taking the results ...
5
votes
3answers
27k views

SMOTE and multi class oversampling

I have read that the SMOTE package is implemented for binary classification. In the case of n classes, it creates additional examples for the smallest class. Can I balance all the classes by running ...
8
votes
1answer
1k views

What is the best performance metric used in balancing dataset using SMOTE technique

I used smote technique to oversample my dataset and now I have a balanced dataset. The problem I faced is that the performance metrics; precision, recall, f1 measure, accuracy in the imbalanced ...
1
vote
0answers
986 views

Can SMOTE be applied over sequence of words (sentences)?

I have a highly unbalanced text classification data. I am trying to over-sample through SMOTE. I have a doubt that applying SMOTE over sequence of word indices will give me valid data points or not (...
2
votes
1answer
254 views

SMOTE oversampling for class imbalanced dataset introduces bias in final distribution

I have a problem statement where percentage of goods (denoted by 0) is 95%, and for bads (denoted by 1) it is 5% only. One way is to do under sampling of goods so that model understands the patterns ...