SMOTE with missing values

Question

I am trying to use SMOTE from imblearn package in Python, but my data has a lot of missing values and I got the following error:

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

I checked the parameters here, and it seems that there is not one dealing with missing value.

Is there a way to generate synthetic samples with missing values?

Answer 1

SMOTE does not perform filling up your missing or NaN values. You need to fill them up and then feed for SMOTE analysis. Dealing with missing values is a different task altogether, you can take a look at Imputer from sklearn to begin with. Here is another write-up on sklearn regarding missing values : Imputing missing values

Once you have finished dealing with NaN values, then feed your modified data to SMOTE.

References

• Predicting missing values with Scikit-learn Imputer module
• Handling missing data with Python
• Handling missing values