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Results tagged with smote
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user 73930
Synthetic Minority Oversampling Technique (SMOTE) is an approach used for dealing with imbalanced datasets before running them through machine learning models.
1
vote
SMOTE Oversampling for Text Classification with Multiple Input Features
you have a ten-dimensional feature space.
obs1 <- c(c(f1_obs1), c(f2_obs1))
obs2 <- c(c(f1_obs2), c(f2_obs2))
Then you can do whatever you want with this constant 10-dimensional feature space, whether SMOTE … Since class imbalance turns out to be a lot less of a problem than is often portrayed, you probably don't need to run the SMOTE, but you will have to combine the features like this or in some similar way …
- 4,244
3
votes
Accepted
How to decide the most suitable technique to handle Class Imbalance
In almost all circumstances, sound statistics disputes that class imbalance is a problem. Consequently, you probably should not do anything besides keep doing good data science that is grounded in cor …
- 4,244