Book Image

Java Data Science Cookbook

By : Rushdi Shams
Book Image

Java Data Science Cookbook

By: Rushdi Shams

Overview of this book

If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data. Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more - things that will come in handy at work.
Table of Contents (16 chapters)
Java Data Science Cookbook
About the Author
About the Reviewer
Customer Feedback

Classifying unseen test data with a filtered classifier

Many times, you will need to use a filter before you develop a classifier. The filter can be used for removing, transforming, discretizing, and adding attributes, removing misclassified instances, randomizing or normalizing instances, and so on. The usual way to do that is to use Weka's Filter class and then perform a series of filtering with the class methods. Besides, Weka has a class named FilteredClassifier, which is a class for running an arbitrary classifier on data that has been passed through an arbitrary filter.

In this recipe, we will see how we can use a filter and a classifier at the same time to classify unseen test examples.

How to do it...

  1. This time, we will be using a Random Forest classifier. As our dataset, we will be using weather.nominal.arff that can be found in the Data directory of the installed Weka folder in your file system.

    The following two will be our instance variables:

            Instances weather = null;