Extremely dominant feature? I'm new to datascience. I was wondering how one should treat an extremely dominant feature.

For example, one of the features is "on"/"off", and when it's "off", none of the other features matter and the output will just always be 0. So should I drop all rows where it's "off" in my train/test data sets? I feel like I would get a better fit that way.

If I delete those rows, I'm concerned about how I would handle those rows in the test set. For example, I'd have to write code to loop through the data and put a 0 in the prediction column for those rows, as well as make sure everything else lines up. (This is all Kaggle related, so the training set is several columns of features and a y_column, whereas the test set doesn't have the y_column and we're supposed to predict it.)

I'm using Python and Scikit Learn's random forest, if that matters.
Rather than discarding the dominant feature (which will discard information), try reducing the number of features randomly selected when making each partition. In scikit's syntax this is max_features (mtry in R's randomForest). By default this is set to compare the square root of all features (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html), instead try much smaller (perhaps even 2 aka decision stump). You'll probably also need many more trees vs. higher settings. This will allow you to extract information from more subtle features without losing all that great information provided by the dominant feature.