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You're reading from Statistics for Machine Learning

Product typeBook

Published inJul 2017

Reading LevelIntermediate

PublisherPackt

ISBN-139781788295758

Edition1st Edition

Languages

Python

Concepts

Statistics

Author (1)

Pratap Dangeti

Ensemble of ensembles with different types of classifiers

As briefly mentioned in the preceding section, different classifiers will be applied on the same training data and the results ensembled either taking majority voting or applying another classifier (also known as a meta-classifier) fitted on results obtained from individual classifiers. This means, for meta-classifier X, variables would be model outputs and Y variable would be an actual 0/1 result. By doing this, we will obtain the weightage that should be given for each classifier and those weights will be applied accordingly to classify unseen observations. All three methods of application of ensemble of ensembles are shown here:

Majority voting or average: In this method, a simple mode function (classification problem) is applied to select the category with the major number of appearances out of individual classifiers. Whereas, for regression problems, an average will be calculated to compare against actual values.
Method of application...

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Statistics for Machine Learning

Published in: Jul 2017Publisher: PacktISBN-13: 9781788295758

Author (1)

Pratap Dangeti

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
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Ensemble of ensembles with different types of classifiers

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