Predictive model for rare occurrences
Hi fellow RapidMiners,
What kind of model would you suggest I should look into when trying to predict a binary outcome with a very high class imbalance (97/3)? The problem at hand is medical readmission within 30 days for surgery. Any suggestions would be appreciated. Currently I am planning to test the k-NN algorithm looping through different k-values.
Best regards.
What kind of model would you suggest I should look into when trying to predict a binary outcome with a very high class imbalance (97/3)? The problem at hand is medical readmission within 30 days for surgery. Any suggestions would be appreciated. Currently I am planning to test the k-NN algorithm looping through different k-values.
Best regards.
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Thank you Lionel,
I will try using SMOTE. Have used it before with success, although with less imbalanced datasets (typically in the range of 30/70)
I will try using SMOTE. Have used it before with success, although with less imbalanced datasets (typically in the range of 30/70)
You can try weighting instead to balance the classes (although not all ML algorithms support weighting). This might give better results than upsampling with such a small minority class.
Tellcontar120: Great idea! I will have to read up upon weighting in RM though. Thank you for your suggestion.
Take a look at the tutorial for the Generate Weight (Stratification) operator, that should be the one that you would use.
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Hi @Casper72,
In your case, I advice you to preprocess your data by upsampling your dataset before modelling.
For that, you can use the SMOTE Upsampling operator from the Operator Toolbox extension available for free in the MarketPlace.
Regards,
Lionel
In your case, I advice you to preprocess your data by upsampling your dataset before modelling.
For that, you can use the SMOTE Upsampling operator from the Operator Toolbox extension available for free in the MarketPlace.
Regards,
Lionel
You can try weighting instead to balance the classes (although not all ML algorithms support weighting). This might give better results than upsampling with such a small minority class.
Take a look at the tutorial for the Generate Weight (Stratification) operator, that should be the one that you would use.
In your case, I advice you to preprocess your data by upsampling your dataset before modelling.
For that, you can use the SMOTE Upsampling operator from the Operator Toolbox extension available for free in the MarketPlace.
Regards,
Lionel