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


We saw that CART methods trained with ensemble routines are powerful when it comes to predictive accuracy. However, they can be computationally expensive and we have covered some techniques in speeding them up in sklearn's applications. We noticed that using extreme randomized forests tuned with randomized search could speed up by tenfold when used properly. For GBM, however, there is no parallelization implemented in sklearn, and this is exactly where XGBoost comes in.

XGBoost comes with an effective parallelized boosting algorithm speeding up the algorithm nicely. When we use larger files (>100k training examples), there is an out-of-core method that makes sure we don't overload our main memory while training models.

The biggest gains in speed and memory can be found with H2O; we saw powerful tuning capabilities together with an impressive training speed.