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
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Hyperparameter tuning


As in batch learning, there are no shortcuts in out-of-core algorithms when testing the best combinations of hyperparameters; you need to try a certain number of combinations to figure out a possible optimal solution and use an out-of-sample error measurement to evaluate their performance.

As you actually do not know if your prediction problem has a simple smooth convex loss or a more complicated one and you do not know exactly how your hyperparameters interact with each other, it is very easy to get stuck into some sub-optimal local-minimum if not enough combinations are tried. Unfortunately, at the moment there are no specialized optimization procedures offered by Scikit-learn for out-of-core algorithms. Given the necessarily long time to train an SGD on a long stream, tuning the hyperparameters can really become a bottleneck when building a model on your data using such techniques.

Here, we present a few rules of thumb that can help you save time and efforts and achieve...