Book Image

PySpark Cookbook

By : Denny Lee, Tomasz Drabas
Book Image

PySpark Cookbook

By: Denny Lee, Tomasz Drabas

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.
Table of Contents (13 chapters)
Title Page
Packt Upsell

Selecting the most predictable features

A mantra of (almost) every data scientist is: build a simple model while explaining as much variance in the target as possible. In other words, you can build a model with all your features, but the model may be highly complex and prone to overfitting. What's more, if one of the variables is missing, the whole model might produce an erroneous output and some of the variables might simply be unnecessary, as other variables would already explain the same portion of the variance (a term called collinearity).

In this recipe, we will learn how to select the best predicting model when building either classification or regression models. We will be reusing what we learn in this recipe in the recipes that follow.

Getting ready

To execute this recipe, you will need a working Spark environment and you would have already loaded the data into the forest DataFrame.

No other prerequisites are required.

How to do it...

Let's begin with a code that will help to select the...