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CRC Press Supervised Machine Learning: Optimization Framework - SAS and R

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Description

This comprehensive guide addresses the critical bias-variance tradeoff in supervised learning by presenting a structured optimization framework for real-world machine learning applications. The book combines bootstrapping techniques to generate diverse training and testing datasets, statistical experiment design to identify optimal feature subsets and hyperparameters, and data contamination methods to evaluate classifier robustness. Rather than relying on machine learning methods alone, this resource from CRC Press provides practitioners with systematic approaches to build classifiers that generalise effectively to new data. Written for professionals using SAS and R, it bridges the gap between theoretical concepts and practical implementation, offering actionable strategies to improve model performance and reliability across business, finance, and insurance applications.

Key Features

The supervised machine learning framework uses bootstrapping to create multiple training and testing datasets with varied characteristics for comprehensive model validation.

Statistical experiment design methods identify optimal feature subsets and hyperparameters to maximise machine learning method performance.

Data contamination testing strategies assess classifier robustness and reliability when encountering noisy or unexpected real-world data.

SAS and R implementations provide code examples and practical guidance for applying the optimisation framework in professional settings.

The book addresses how using machine learning methods alone is insufficient for building generalised classifiers without proper optimisation and testing protocols.

Product Specifications

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

Barcode

No barcode data available

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