'Predicting with SMOTE

If I have a training dataset that has 1083 samples and a testing dataset that has 79871 samples, how do I go about making the samples equal? I have been using SMOTE but now I get the error

ValueError: Unknown label type: 'continuous-multioutput'

My code:

from imblearn.over_sampling import SMOTE
from sklearn import utils


over_sampler = SMOTE()

print(utils.multiclass.type_of_target(x))
print(utils.multiclass.type_of_target(ans))

x_over,y_over = over_sampler.fit_resample(x,ans)
y_over = pd.Series(y_over)
y_over

x, ans are the continuous-multioutput

Solution 1:^[1]

Yes sure you can use RandomUnderSampler to downsampling the majority class to be equal with the minority, and SMOTE oversampler to upsampling the minority to be equal with the majority class.

can you explain more about predict certain number of predictions?

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'Predicting with SMOTE

Solution 1:[1]

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Solution 1:^[1]