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Imbalanced data is a big problem in classification problems. I have a binary classification problem with imbalanced data.

I have researched and found that a possible method of dealing with this is preprocessing the data before plugging it into a model. My data looks like this example using iris:

# Create unbalanced binary dataset (Remove 'versicolor')
 data <- iris[-c(95:130),]
 data <- data[data$Species == c("setosa","virginica"),]
 
 
 # Show that data is imbalanced
 table(data$Species) / sum(table(data$Species))

How can I implement

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in R in order to preprocess the data before fitting a model? Which packages could I use particularly for a binary classification problem rather than the multiclass case?

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