Missing values in scikits machine learning
I have come across very similar issue, when running the RandomForestRegressor on data. The presence of NA values were throwing out "nan" for predictions. From scrolling around several discussions, the Documentation by Breiman recommends two solutions for continuous and categorical data respectively.

Calculate the Median of the data from the column(Feature) and use
this (Continuous Data) 
Determine the most frequently occurring Category and use this
(Categorical Data)

According to Breiman the random nature of the algorithm and the number of trees will allow for the correction without too much effect on the accuracy of the prediction. This I feel would be the case if the presence of NA values is sparse, a feature containing many NA values I think will most likely have an affect.
Is it possible to have missing values in scikit-learn ? How should they be represented? I couldn't find any documentation about that.