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Название: Statistics with R for Machine Learning: Vol. 1-2
Автор: Mohsen Nady
Издательство: Arcler Press
Год: 2025
Страниц: 298+188
Язык: английский
Формат: pdf (true)
Размер: 17.0 MB
Data preparation is the foundation of any successful Machine Learning project. This volume provides a comprehensive guide to cleaning, transforming, and splitting data for Machine Learning using R, including handling missing values, feature scaling, and stratified sampling. Practical examples and R code demonstrate how to optimize datasets for predictive modeling. The volume is essential for data scientists and Machine Learning practitioners seeking to build robust models. Resampling techniques are key to improving model performance and reliability in Machine Learning. This volume explores advanced resampling methods, including cross-validation, bootstrapping, and hyperparameter tuning, using R. Readers will learn how to apply these techniques to optimize model accuracy and prevent overfitting. Practical examples and case studies illustrate their real-world applications. This voulme is an essential resource for data scientists and Machine Learning enthusiasts aiming to master resampling strategies.
Автор: Mohsen Nady
Издательство: Arcler Press
Год: 2025
Страниц: 298+188
Язык: английский
Формат: pdf (true)
Размер: 17.0 MB
Data preparation is the foundation of any successful Machine Learning project. This volume provides a comprehensive guide to cleaning, transforming, and splitting data for Machine Learning using R, including handling missing values, feature scaling, and stratified sampling. Practical examples and R code demonstrate how to optimize datasets for predictive modeling. The volume is essential for data scientists and Machine Learning practitioners seeking to build robust models. Resampling techniques are key to improving model performance and reliability in Machine Learning. This volume explores advanced resampling methods, including cross-validation, bootstrapping, and hyperparameter tuning, using R. Readers will learn how to apply these techniques to optimize model accuracy and prevent overfitting. Practical examples and case studies illustrate their real-world applications. This voulme is an essential resource for data scientists and Machine Learning enthusiasts aiming to master resampling strategies.