Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, adn substantially extends two decades of research on…
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
