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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.
In Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies that 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.
Ensemble methods are a valuable tool. I can aggregate the strengths from multiple methods while mitigating their individual weaknesses and increasing model performance.
—Noah Flynn, Amazon
With each new chapter the author, Gautam Kunapuli, explains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. No complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation!
Ensemble Methods for Machine Learning is available from its publisher Manning and from Amazon.
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