Book Image

Machine Learning with Spark - Second Edition

By : Rajdeep Dua, Manpreet Singh Ghotra
Book Image

Machine Learning with Spark - Second Edition

By: Rajdeep Dua, Manpreet Singh Ghotra

Overview of this book

This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.
Table of Contents (13 chapters)

Extracting the right features from your data

Like most of the machine learning models we have encountered so far, k-means clustering requires numerical vectors as input. The same feature extraction and transformation approaches that we have seen for classification and regression are applicable for clustering.

As k-means, like least squares regression, uses a squared error function as the optimization objective, it tends to be impacted by outliers and features with large variance.

Clustering could be leveraged to detect outliers as they can cause a lot of problems.

As for regression and classification cases, input data can be normalized and standardized to overcome this, which might improve accuracy. In some cases, however, it might be desirable not to standardize data, if, for example, the objective is to find segmentations according to certain specific features.

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