£94.83

CRC Press Supervised Machine Learning - SAS and R Optimization Book

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Description

Improve your machine learning models with this specialized AI framework designed to address the bias-variance tradeoff in real-life applications. This book provides a structured approach to supervised learning, moving beyond simple model implementation to focus on true generalization for new data. By utilizing bootstrapping techniques, the framework allows for the creation of multiple training and testing datasets with diverse characteristics. Readers will learn to design and analyze statistical experiments to identify the most effective feature subsets and optimal hyper-parameters for various ML methods. Additionally, the text covers data contamination techniques to test the robustness of your classifiers. Whether you are working with SAS or R, this resource offers a practical methodology for building reliable, high-performing models that perform well outside of training environments. It is an essential guide for those looking to master the complexities of model optimization and statistical experiment design.

Key Features

Solves the bias-variance tradeoff problem to help build supervised learning models that work effectively in real-life applications.

Uses bootstrapping methods to create multiple training and testing datasets with various characteristics for better model validation.

Includes design and analysis of statistical experiments to identify the best feature subsets and optimal hyper-parameters.

Provides techniques for data contamination to test how robust your classifiers are against unexpected data changes.

Focuses on building classifiers that generalize well to new data rather than just performing well on training sets.

Offers practical implementation guidance for machine learning methods using both SAS and R software.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
22 September 2020
Listed Since
01 April 2020

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