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Ensemble Methods for Machine Learning
Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate.
Inside Ensemble Methods for Machine Learning you will
Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a “wisdom of crowds” method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets.
About the Book
Ensemble Methods for Machine Learning teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There’s no complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation!
What’s Inside
About the Reader
For Python programmers with machine learning experience.
About the Author
Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry.
Table of Contents
PART 1 - THE BASICS OF ENSEMBLES
1 Ensemble Hype or hallelujah?
PART 2 - ESSENTIAL ENSEMBLE METHODS
2 Homogeneous parallel Bagging and random forests
3 Heterogeneous parallel Combining strong learners
4 Sequential Adaptive boosting
5 Sequential Gradient boosting
6 Sequential Newton boosting
PART 3 - ENSEMBLES IN THE ADAPTING ENSEMBLE METHODS TO YOUR DATA
7 Learning with continuous and count labels
8 Learning with categorical features
9 Explaining your ensembles
Inside Ensemble Methods for Machine Learning you will
Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a “wisdom of crowds” method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets.
About the Book
Ensemble Methods for Machine Learning teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There’s no complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation!
What’s Inside
About the Reader
For Python programmers with machine learning experience.
About the Author
Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry.
Table of Contents
PART 1 - THE BASICS OF ENSEMBLES
1 Ensemble Hype or hallelujah?
PART 2 - ESSENTIAL ENSEMBLE METHODS
2 Homogeneous parallel Bagging and random forests
3 Heterogeneous parallel Combining strong learners
4 Sequential Adaptive boosting
5 Sequential Gradient boosting
6 Sequential Newton boosting
PART 3 - ENSEMBLES IN THE ADAPTING ENSEMBLE METHODS TO YOUR DATA
7 Learning with continuous and count labels
8 Learning with categorical features
9 Explaining your ensembles
352 pages, Paperback
Published May 2, 2023
2 people are currently reading
17 people want to read
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Displaying 1 - 2 of 2 reviews
May 11, 2023
Engaging examples to explain the Why of ensemble learning.
Code examples of ensemble machine learning along with graphical representation of evaluation metrics
terminology and types for ensemble methods and how they work
Ways to perform model aggregation
It was fun to see how a randomforest works, something we use so often.
Exhaustive coverage of both heterogeneous and parallel ensembles and ways of combining predictions
The book also covers loss functions
Real world examples and case studies
I used to go though kaggle solutions which used ensemble models and wondered about the reasoning behind choosing specific ensemble models.
This book can be helpful to follow through
Covers ensemble to both classification and regression, also handling categorical features, explaining models
Code examples of ensemble machine learning along with graphical representation of evaluation metrics
terminology and types for ensemble methods and how they work
Ways to perform model aggregation
It was fun to see how a randomforest works, something we use so often.
Exhaustive coverage of both heterogeneous and parallel ensembles and ways of combining predictions
The book also covers loss functions
Real world examples and case studies
I used to go though kaggle solutions which used ensemble models and wondered about the reasoning behind choosing specific ensemble models.
This book can be helpful to follow through
Covers ensemble to both classification and regression, also handling categorical features, explaining models
May 19, 2024
It was highly interesting book to explore. Good for anyone who has advanced level of machine learning knowledge. top-notch.
Displaying 1 - 2 of 2 reviews