Items related to Ensemble Methods for Machine Learning
Synopsis
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 find:
- Methods for classification, regression, and recommendations
- Sophisticated off-the-shelf ensemble implementations
- Random forests, boosting, and gradient boosting
- Feature engineering and ensemble diversity
- Interpretability and explainability for ensemble methods
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.
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
- Bagging, boosting, and gradient boosting
- Methods for classification, regression, and retrieval
- Interpretability and explainability for ensemble methods
- Feature engineering and ensemble diversity
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 methods: Hype or hallelujah?
PART 2 - ESSENTIAL ENSEMBLE METHODS
2 Homogeneous parallel ensembles: Bagging and random forests
3 Heterogeneous parallel ensembles: Combining strong learners
4 Sequential ensembles: Adaptive boosting
5 Sequential ensembles: Gradient boosting
6 Sequential ensembles: Newton boosting
PART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA
7 Learning with continuous and count labels
8 Learning with categorical features
9 Explaining your ensembles
"synopsis" may belong to another edition of this title.
About the Author
Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. He has developed several novel algorithms for diverse application domains including social network analysis, text and natural language processing, behavior mining, educational data mining and biomedical applications. He has also published papers exploring ensemble methods in relational domains and with imbalanced data.
From the Back Cover
In Ensemble Methods for Machine Learning you'll learn to implement the most important ensemble machine learning methods from scratch. Each chapter contains a new case study, taking you hands-on with a fully functioning ensemble method for medical diagnosis, sentiment analysis, handwriting classification, and more. There's no complex math or theory--each method is taught in a practical and visuals-first manner. Best of all, all code is provided in Jupyter notebooks for your easy experimentation! 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.
"About this title" may belong to another edition of this title.
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Paperback. Condition: Good. Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results. Seller Inventory # CIN1617297135G
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