Can you fix the false negative rate in a classifier in scikit learn

Question

I am using a Random Forest classifer in scikit learn with an imbalanced data set of two classes. I am much more worried about false negatives than false positives. Is it possible to fix the false negative rate (to, say, 1%) and ask scikit to optimize the false positive rate somehow?

If this classifier doesn't support it, is there another classifier that does?

Answer 1

I believe the problem of class imbalance in sklearn can be partially resolved by using the class_weight parameter.

this parameter is either a dictionary, where each class is assigned a uniform weight, or is a string that tells sklearn how to build this dictionary. For instance, setting this parameter to 'auto', will weight each class in proportion of the inverse of its frequency.

By weighting the class that is less present with a higher amount, you can end up with 'better' results.

Classifier like like SVM or logistic regression also offer this class_weight parameter.

This Stack Overflow answer gives some other ideas on how to handle class imbalance, like under sampling and oversampling.

Answer 2

I found this article on class imbalance problem.

http://www.chioka.in/class-imbalance-problem/

It has basically discussed the following possible solutions to summarize:

• Cost function based approaches
• Sampling based approaches
• SMOTE (Synthetic Minority Over-Sampling Technique)
• recent approaches : RUSBoost, SMOTEBagging and Underbagging

Hope It may help.