Book Image

Mastering Machine Learning with Spark 2.x

By : Michal Malohlava, Alex Tellez, Max Pumperla
Book Image

Mastering Machine Learning with Spark 2.x

By: Michal Malohlava, Alex Tellez, Max Pumperla

Overview of this book

The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.
Table of Contents (9 chapters)
3
Ensemble Methods for Multi-Class Classification

Summary

This chapter introduced several important concepts including data cleanup and handling missing and categorical values, using Spark and H2O to train multi-classification models, and various evaluation metrics for classification models. Furthermore, the chapter brings the notion of model ensembles demonstrated on RandomForest as the ensemble of decision trees.

The reader should see the importance of data preparation, which plays a key role during every model training and evaluation process. Training and using a model without understanding the modeling context can lead to misleading decisions. Moreover, every model needs evaluation with respect to the modeling goal (for example, minimization of false positives). Hence understanding trade-offs of different model metrics of classification models is crucial.

In this chapter, we did not cover all possible modelling tricks for...