Book Image

Large Scale Machine Learning with Python

By : Bastiaan Sjardin, Alberto Boschetti
Book Image

Large Scale Machine Learning with Python

By: Bastiaan Sjardin, Alberto Boschetti

Overview of this book

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
Table of Contents (17 chapters)
Large Scale Machine Learning with Python
About the Authors
About the Reviewer

Fast parameter optimization with randomized search

You might already be familiar with Scikit-learn's gridsearch functionalities. It is a great tool but when it comes to large files, it can ramp up training time enormously depending on the parameter space. For extreme random forests, we can speed up the computation time for parameter tuning using an alternative parameter search method named randomized search. Where common gridsearch taxes both CPU and memory by systematically testing all possible combinations of the hyperparameter settings, randomized search selects combinations of hyperparameters at random. This method can lead to a considerable computational speedup when the gridsearch is testing more than 30 combinations (for smaller search spaces, gridsearch is still competitive). The gain achievable is in the same order as we have seen when we switched from random forests to extremely randomized forests (think between a two to tenfold gain, depending on hardware specifications, hyperparameter...