Random forest model gives same result for all test data, Next step? I am teaching myself some data science and have started a Kaggle project. 
I have fitted a random forest classification model (using sci-kit learn) with a few millions rows of data. There are two possible outcomes for each row (0 or 1). When I run it against the test data, I get 0 for every row. This is a practical impossibility, but I am at a loss as to how to diagnose my model and how to move forward. 
Is this an example of extreme overfitting ? Is it more likely a problem with my training data (or test data) that i am not seeing ?

Should I simply increase the # of estimators? Is it possible that I have misformatted the input files in a subtle way that doesn't cause an error ? I am at a loss as to how to move forward.
Some of the possibilities include the following:

1) The training data has class imbalance.

Solution:


Train the model using CV = 5 or 10;
Do a log transformation to make the target distribution more normal in nature;
Check if you can add a weight variable that can fix the distribution.


2) Testing data is a small sub sample. Since it's Kaggle, I believe the testing data will be standard in nature, so the problem might be in the process that is generating the submission script.

As a beginner, you should use any benchmark script available in the forum to start with, which shouldn't have such issues.