Loading a dataset into memory, preparing a data matrix, training a machine learning algorithm, and testing its generalization capabilities using out-of-sample observations are often not such a big deal given the quite powerful and yet affordable computers of this day and age. However, more and more frequently, the scale of the data to be elaborated is so huge that loading it into the core memory of your computer is not possible and, even if manageable, the result is intractable both in terms of data management and machine learning.
Alternative viable strategies beyond the core memory processing are possible: splitting the data into samples, using parallelism, and finally learning in small batches or by single instances. The present chapter will focus on the out-of-the-box solution that the Scikit-learn package offers: the streaming of mini batches of instances (our observations) from data storage and the incremental learning based on them. Such...