datas-frame – Scalable Machine Learning (Part 2): Partial Fit

This work is supported by Anaconda, Inc. and the Data Driven Discovery Initiative from the Moore Foundation. This is part two of my series on scalable machine learning. Scikit-learn supports out-of-core learning (fitting a model on a dataset that doesn't fit in RAM), through it's partial_fit API. The basic idea is that, for certain estimators, learning can be done in batches. The estimator will see a batch, and then incrementally update whatever it's learning (the coefficients, for example).