Ensemble Methods For Machine Learning Av Gautam Kunapuli

<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:<BR><BR><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 t