How to improve an existing machine learning classifier in python?
The simplest, and likely best, way is to just rebuild the model every time you add more data.

If you want to build on existing models, one way is to average multiple random forest models.  So you build a model on 20k, test it, then decide to build on it by building another random forest model on a separate set of 20k and averaging the model parameter results with the previous model.  This is basically a poorly described ensemble method.  Check out this: http://scikit-learn.org/stable/modules/ensemble.html.