title: Statsmodels Python - Weighted GLM
body: <p>I am currently working with significantly imbalanced data using the statsmodel package GLM (Or the separate logit function if need be). Thus far I have not found a way to implement instance weighting in these methods, however I heard that the current dev release of 0.7 may have this functionality. </p>
  
  <p>1) Is there a way to implement sample weighting in the current stable release
  2) If not has the current 0.7-dev release implemented this feature yet?</p>
  
  <p>While I know I can manually over/under sample the data I like the ability to weight classes as under sampling creates a situation where you intentionally lose training data, and over sampling may not be truly representative of the population. </p>
  
  <p>Even if it does not the functionality to balance the classes internally SK-learn's balance class function can be used in conjunction with the instance weighting to achieve the same functionality 
  (<a href="http://jaquesgrobler.github.io/online-sklearn-build/modules/generated/sklearn.preprocessing.balance_weights.html" rel="nofollow">http://jaquesgrobler.github.io/online-sklearn-build/modules/generated/sklearn.preprocessing.balance_weights.html</a>)</p>
  
  <p>Thank you</p>