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Ensemble machine learning trains a diverse group of machine learning models to work together and aggregate their output to provide a richer set of results than a single model.
Ensemble Methods for Machine Learning lets you discover core ensemble methods proven in both data science competitions and real-world applications. Hands-on case studies showing how each algorithm works in production. By the time you’re done, you’ll understand the benefits, limitations, and practical ways to apply ensemble machine learning to real-world data, and be ready to build more explainable ML systems.
The ensemble method is a valuable tool. Combine the strengths of multiple methods while mitigating individual weaknesses and improving model performance.
— Noah Flynn, Amazon
In each new chapter, author Gautam Kunapuli presents a unique case study that demonstrates a fully functional ensemble method using examples from medical diagnosis, sentiment analysis, handwriting classification, and more. No complicated math or theory required. Learn visually with rich code for easy experimentation.
Ensemble Methods for Machine Learning is available from publishers Manning and Amazon.
35% discount code (valid for all products in all formats): nlkdnuggets21
1 free eBook code for Ensemble Methods for Machine Learning: enskdrf-413E
