{ "item_title" : "Ensemble Methods for Machine Learning", "item_author" : [" Gautam Kunapuli "], "item_description" : "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 recommendationsSophisticated off-the-shelf ensemble implementationsRandom forests, boosting, and gradient boostingFeature engineering and ensemble diversityInterpretability and explainability for ensemble methodsEnsemble 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 InsideBagging, boosting, and gradient boostingMethods for classification, regression, and retrievalInterpretability and explainability for ensemble methodsFeature engineering and ensemble diversityAbout 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 ENSEMBLES1 Ensemble methods: Hype or hallelujah?PART 2 - ESSENTIAL ENSEMBLE METHODS2 Homogeneous parallel ensembles: Bagging and random forests3 Heterogeneous parallel ensembles: Combining strong learners4 Sequential ensembles: Adaptive boosting5 Sequential ensembles: Gradient boosting6 Sequential ensembles: Newton boostingPART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA7 Learning with continuous and count labels8 Learning with categorical features9 Explaining your ensembles", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/61/729/713/1617297135_b.jpg", "price_data" : { "retail_price" : "59.99", "online_price" : "59.99", "our_price" : "59.99", "club_price" : "59.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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