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 have just discussed that there are no options for parallel processing when using GBM from Scikit-learn, and this is exactly where XGBoost comes in. Expanding on GBM, XGBoost introduces more scalable methods leveraging multithreading on a single machine and parallel processing on clusters of multiple servers (using sharding). The most important improvement of XGBoost over GBM lies in the capability of the latter to manage sparse data. XGBoost automatically accepts sparse data as input without storing zero values in memory. A second benefit of XGBoost lies in the way in which the best node split values are calculated while branching the tree, a method named quantile sketch. This method transforms the data by a weighting algorithm so that candidate splits are sorted based on a certain accuracy level. For more information read the article at

XGBoost stands for Extreme Gradient Boosting, an open source gradient boosting algorithm that has gained...