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

Random forest and extremely randomized forest

Apart from bagging, based on training examples, we can also draw random subsamples based on features. Such a method is referred to as Random Subspaces. Random subspaces are particularly useful for high-dimensional data (data with lots of features) and it is the foundation of the method that we refer to as random forest. At the time of writing this, random forest is the most popular machine learning algorithm because of its ease of use, robustness to messy data, and parallelizability. It found its way into all sorts of applications such as location apps, games, and screening methods for healthcare applications. For instance, the Xbox Kinect uses a random forest model for motion detection purposes. Considering that the random forest algorithm is based on bagging methods, the algorithm is relatively straightforward:

  1. Bootstrap m samples of size N from the available sample.

  2. Trees are constructed independently on each subset (S1, S2, … Sn) using a different...