generate synthetic dataset with rose in r

Installing "rose" package

In order to generate synthetic datasets with "rose", we first need to install the package in R. This can be done using the following command:

main.r
install.packages("rose")
25 chars
2 lines

Generating Synthetic Dataset with "rose"

The "rose" package provides the function rose() that can be used to generate synthetic datasets. This function requires the original dataset as input, along with a few other parameters that define the characteristics of the synthetic dataset to be generated.

Here is an example of how to use the rose() function to generate a synthetic dataset:

main.r
#load the rose library
library(rose)

#load the original dataset
data(original_dataset)

#generate a synthetic dataset with rose
synthetic_dataset <- rose(original_dataset, frac = 1, p = 0.5)
192 chars
9 lines

In the code above, we first loaded the rose package using the library() function. We then loaded the original dataset into R (assuming it was already imported or created). We finally called the rose() function using the original_dataset as input along with the desired fraction of minority class instances (here set to 1) and the desired probability of generating a synthetic example using the ROSE algorithm (here set to 0.5).

The resulting synthetic_dataset will contain the same number of observations as the original dataset but with the minority class artificially oversampled to the desired fraction.

Conclusion

Generating synthetic datasets can be useful in many situations, especially when you have a class imbalance problem. The rose package provides an easy-to-use function that can generate synthetic datasets with balanced classes.