{"product_id":"ensemble-methods-for-machine-learning","title":"Ensemble Methods for Machine Learning","description":"\u003cp\u003e\u003cstrong\u003eInside \u003ci\u003eEnsemble Methods for Machine Learning\u003c\/i\u003e you will find:\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eMethods for classification, regression, and recommendations\u003c\/li\u003e\n\u003cli\u003eSophisticated off-the-shelf ensemble implementations\u003c\/li\u003e\n\u003cli\u003eRandom forests, boosting, and gradient boosting\u003c\/li\u003e\n\u003cli\u003eFeature engineering and ensemble diversity\u003c\/li\u003e\n\u003cli\u003eInterpretability and explainability for ensemble methods\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\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.\u003c\/p\u003e\u003ch2\u003eabout the technology\u003c\/h2\u003e\u003cp\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.\u003c\/p\u003e\u003ch2\u003eabout the book\u003c\/h2\u003e\u003cp\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!\u003c\/p\u003e\u003ch2\u003ewhat's inside\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003eBagging, boosting, and gradient boosting\u003c\/li\u003e\n\u003cli\u003eMethods for classification, regression, and retrieval\u003c\/li\u003e\n\u003cli\u003eInterpretability and explainability for ensemble methods\u003c\/li\u003e\n\u003cli\u003eFeature engineering and ensemble diversity\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eabout the reader\u003c\/h2\u003e\u003cp\u003eFor Python programmers with machine learning experience.\u003c\/p\u003e\u003ch3\u003eAbout the Author\u003c\/h3\u003e\u003cp\u003e\u003cb\u003eGautam Kunapuli\u003c\/b\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePART 1 - THE BASICS OF ENSEMBLES\u003c\/b\u003e\u003cbr\u003e1 Ensemble methods: Hype or hallelujah?\u003c\/p\u003e\u003cp\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\u003c\/p\u003e\u003cp\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\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEbook License\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eEnd-User Warranty And License Agreement\u003c\/p\u003e\u003cp\u003e1. 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