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Java Data Science Cookbook

You're reading from   Java Data Science Cookbook Explore the power of MLlib, DL4j, Weka, and more

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787122536
Length 372 pages
Edition 1st Edition
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Author (1):
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Rushdi Shams Rushdi Shams
Author Profile Icon Rushdi Shams
Rushdi Shams
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Toc

Table of Contents (10) Chapters Close

Preface 1. Obtaining and Cleaning Data FREE CHAPTER 2. Indexing and Searching Data 3. Analyzing Data Statistically 4. Learning from Data - Part 1 5. Learning from Data - Part 2 6. Retrieving Information from Text Data 7. Handling Big Data 8. Learn Deeply from Data 9. Visualizing Data

Generating logistic regression models


Weka has a class named Logistic, which can be used for building and using a multinomial logistic regression model with a ridge estimator. Although the original logistic regression does not deal with instance weights, the algorithm in Weka has been modified to handle the instance weights.

In this recipe, we will use Weka to generate a logistic regression model on the iris dataset.

How to do it...

  1. We will be generating a logistic regression model from the iris dataset, which can be found in the data directory in the installed folder of Weka.

    Our code will have two instance variables: one will be containing the data instances of the iris dataset, and the other will be the logistic regression classifier:

            Instances iris = null; 
            Logistic logReg ; 
    
  2. We will be using a method to load and read the dataset, as well as to assign its class attribute (the last attribute of the iris.arff file):

            public void loadArff(String arffInput){ &...
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