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When I use SMOTE-NC to oversample three classes of a 4-class classification problem, the Prec, Recall, and F1 metrics for minority classes are still VERY low (~3%). I have 32 categorical and 30 continuous variables in my dataset. All the categorical variables have been converted to binary columns using one-hot encoding. Also, before going for the over-sampling process, I am imputing all missing values using Iterativeimputer.

Regarding the classifiers, I am using logistic regression, random forest and XGboost. May I have your thoughts on this? Any suggestions to over-sample a multiclass and highly imbalanced dataset?

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    $\begingroup$ First of all, one-hot encoding is generally not recommended for tree-based methods. I would use OrdinalEncoder from sklearn instead. Secondly, what is your class distribution (what is the % of each class in your data)? As @georg-un pointed out scaling weights can be helpful sometimes. What are you setting class_weights to? $\endgroup$
    – jared3412341
    Commented Oct 3, 2020 at 15:07

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Before going through the process of oversampling, always see if the implementation of your algorithm supports assigning different weights to individual classes. The sklearn RandomForestClassifier has for example a class_weights parameter with which you can do that. I found this method to work better than over- or undersampling.

Also, I have to add an obligatory part: if you minority classes have only very few samples so that the charachteristics of the respective classes are not well captured, there is little you can do except collecting more data.

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  • $\begingroup$ Thank you for your suggestion, I tried the class_weight parameter, no improvement! The final AUC numbers are high enough (~90%) to be able to say the models have enough discrimination power, but I thought there is no point in a high AUC when you have very low precision/recall. $\endgroup$
    – Sarah
    Commented Aug 10, 2019 at 17:02

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