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how to use feature engineering on a label
1 votes

First thing first, you should remove all the space from the columns, this would create problems when you have written enough code and one mistake in spacing would stop the program from running. So ...

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Subsampling the “right” amout of data to train an ML model
0 votes

Every Machine Learning Problem is different, so there is no standard answer to your question. For the problem you're working on maybe a 70-30 train-test split would result in an optimal model which ...

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Is it acceptable to use label encoding for nominal categorical data when one hot encoding would create too many features?
0 votes

Tree Based models like RF and XGBoost can handle the feature space created by either of the methods you suggested above. Additionally, you can try to do feature selection (use those statistical tests) ...

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Usage of Precision Recall on an unbalanced dataset
-1 votes

Found a solution, for an unbalanced dataset, first use SMOTE and then apply any model to use check AUC

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