How to improve an existing machine learning classifier in python? I have a big dataset (1million x 50) to which I want to predict a particular class. I have thought of segregating the dataset in batches of 20k. And then train a classifier (lets say random forest or a basic SVM). How do I then improve that classifier by providing it with additional dataset. In other words, how do I preserve the random forests created in iteration i and use as starting model in interation i+1 to improve the model 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.