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Oversampling before Cross-Validation, is it a problem?
2 votes

I suggest having a read of this article. The article explains: When upsampling before cross validation, you will be picking the most oversampled model, because the oversampling is allowing data to ...

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Setting sparse=True in Scikit Learn OneHotEncoder does not reduce memory usage
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1 votes

Based on @BenReiniger's comment, I removed the numeric portion from the ColumnTransformer and ran the following code: from sklearn.compose import ColumnTransformer from sklearn.preprocessing ...

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Visualising feature selection results for multiple classifiers and feature subset sizes
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0 votes

Thank you for all the suggested answers, they helped. What I ended up doing is the following: Firstly I changed my function that compares between the different models like so: def ...

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