Ensemble Methods for Machine Learning

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Beschreibung

<b>Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate.</b><br><br>Inside <i>Ensemble Methods for Machine Learning</i> you will find:<ul> <li>Methods for classification, regression, and recommendations</li> <li>Sophisticated off-the-shelf ensemble implementations</li> <li>Random forests, boosting, and gradient boosting</li> <li>Feature engineering and ensemble diversity</li> <li>Interpretability and explainability for ensemble methods</li> </ul><br>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 <i>Ensemble Methods for Machine Learning</i> 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.<br><br><b>About the Technology </b><br><br>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.<br><br><b>About the Book </b><br><br><i>Ensemble Methods for Machine Learning</i> 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—yo

Artikeldetails

EAN

9781617297137

Sprache

Englisch

Einband / Art

Taschenbuch

Maße

231 x 185 x 20 mm

Erscheinungsjahr

2023

Verlag / Hersteller

Manning