This is just a question about Classimbalance.jl's ability to handle different size datasets. I was working with the python imbalance-learn package, and it keeps crashing when I give it a dataset of more than 2-3 million rows. In the case of imbalanced data, this is to be expected since it takes so many false examples to get a positive one. I can find creative ways to "thin" the dataset, but I was just wondering if there were any tests on how the julia package handles larger datasets?
Thanks.
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Describe the bug
This is just a question about
Classimbalance.jl
's ability to handle different size datasets. I was working with the pythonimbalance-learn
package, and it keeps crashing when I give it a dataset of more than 2-3 million rows. In the case of imbalanced data, this is to be expected since it takes so many false examples to get a positive one. I can find creative ways to "thin" the dataset, but I was just wondering if there were any tests on how the julia package handles larger datasets?Thanks.
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Desktop (please complete the following information):
Smartphone (please complete the following information):
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