How do you handle class imbalance problems and presence of outliers?

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Accepted Answer

I handle class imbalance by using techniques like oversampling, undersampling, and SMOTE. For outliers, I use statistical methods like Z-score and IQR.

For class imbalance, I use oversampling techniques like SMOTE or undersampling techniques like random undersampling.
I also use ensemble methods like bagging and boosting to handle class imbalance.
For outliers, I use statistical methods like Z-score and IQR to detect and remove them.
I also use visualization techniques like box plots and scatter plots to identify outliers.
In some cases, I may choose to keep outliers if they are not errors but rather important data points.
I also use robust statistical methods like median and MAD instead of mean and standard deviation to handle outliers.

How do you handle class imbalance problems and presence of outliers?

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