{"product_id":"9781617297137","title":"Ensemble Methods for Machine Learning","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eKunapuli, Gautam\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e5\/2\/2023\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eEnsemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eInside \u003ci\u003eEnsemble Methods for Machine Learning\u003c\/i\u003e you will find:\u003cul\u003e \u003cli\u003eMethods for classification, regression, and recommendations\u003c\/li\u003e \u003cli\u003eSophisticated off-the-shelf ensemble implementations\u003c\/li\u003e \u003cli\u003eRandom forests, boosting, and gradient boosting\u003c\/li\u003e \u003cli\u003eFeature engineering and ensemble diversity\u003c\/li\u003e \u003cli\u003eInterpretability and explainability for ensemble methods\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003eEnsemble 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 \u003ci\u003eEnsemble Methods for Machine Learning\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Technology \u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eAutomatically 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Book \u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003ci\u003eEnsemble Methods for Machine Learning\u003c\/i\u003e 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!\u003cbr\u003e\u003cbr\u003e\u003cb\u003eWhat’s Inside \u003c\/b\u003e\u003cul\u003e \u003cli\u003eBagging, boosting, and gradient boosting\u003c\/li\u003e \u003cli\u003eMethods for classification, regression, and retrieval\u003c\/li\u003e \u003cli\u003eInterpretability and explainability for ensemble methods\u003c\/li\u003e \u003cli\u003eFeature engineering and ensemble diversity\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003cb\u003eAbout the Reader\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eFor Python programmers with machine learning experience.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author \u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eGautam Kunapuli has over 15 years of experience in academia and the machine learning industry.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePART 1 - THE BASICS OF ENSEMBLES\u003c\/b\u003e\u003cbr\u003e1 Ensemble methods: Hype or hallelujah?\u003cbr\u003e\u003cb\u003ePART 2 - ESSENTIAL ENSEMBLE METHODS\u003c\/b\u003e\u003cbr\u003e2 Homogeneous parallel ensembles: Bagging and random forests\u003cbr\u003e3 Heterogeneous parallel ensembles: Combining strong learners\u003cbr\u003e4 Sequential ensembles: Adaptive boosting\u003cbr\u003e5 Sequential ensembles: Gradient boosting\u003cbr\u003e6 Sequential ensembles: Newton boosting\u003cbr\u003e\u003cb\u003ePART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA\u003c\/b\u003e\u003cbr\u003e7 Learning with continuous and count labels\u003cbr\u003e8 Learning with categorical features\u003cbr\u003e9 Explaining your ensembles","brand":"Manning","offers":[{"title":"Default Title","offer_id":48172273598719,"sku":"9781617297137","price":59.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_d287082d-691a-4133-9095-a40d04a269c7.jpg?v=1771253720","url":"https:\/\/massivebookshop.com\/products\/9781617297137","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}