Many algorithms for imbalanced data support binary and multiclass classification only. This approach is made for mulit-label classification (aka multi-target classification).

• Multilabel dataset (as pandas.DataFrame) with imbalanced data
• Calculate counts per class and then calculate the standard deviation (std) of the count values
• Do for number_of_adds times the following:
• Randomly draw a sample from your data and calculate new std
  • If new std reduces, add sample to your dataset
  • If not, draw another sample (to this up to number_of_tries times)
• A new df is returned.
• A result plot viszualize the target distribition before and after upsampling. Moreover the counts per index are shown.

from multilabel_oversampling import multilabel_oversampling as mo

df = mo.create_fake_data(size=1, seed=3)
ml_oversampler = mo.MultilabelOversampler(number_of_adds=100, number_of_tries=100)
df_new, plot_at = ml_oversampler.fit(df)
#> Iteration:  20%|██████                        | 20/100 [00:00<00:00, 111.68it/s]
#> No improvement after 100 tries in iter 20.

ml_oversampler.plot_results()

• Install from GitHub

pip install git+https://github.com/phiyodr/multilabel-oversampling

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