12 Machine Learning
12.1 Hands-On Machine Learning with R
Bradley Boehmke & Brandon Greenwell
This book provides hands-on modules for many of the most common machine learning methods to include:
Generalized low rank models, Clustering algorithms, Autoencoders, Regularized models, Random forests, Gradient boosting machines, Deep neural networks, Stacking / super learners and more!
12.2 Feature Engineering and Selection: A Practical Approach for Predictive Models
Max Kuhn and Kjell Johnson
The goals of Feature Engineering and Selection are to provide tools for re-representing predictors, to place these tools in the context of a good predictive modeling framework, and to convey our experience of utilizing these tools in practice.
12.3 Interpretable Machine Learning
A Guide for Making Black Box Models Explainable
Pay what you want for the ebook, minimum $0.00
12.4 Explanatory Model Analysis
Responsible, Fair and Explainable Predictive Modeling with examples in R and Python
Przemyslaw Biecek, Tomasz Burzykowski
Free online: https://pbiecek.github.io/ema/
Or paid printed: https://www.routledge.com/Explanatory-Model-Analysis-Explore-Explain-and-Examine-Predictive-Models/Biecek-Burzykowski/p/book/9780367135591
12.5 Supervised Machine Learning for Text Analysis in R
Modeling as a statistical practice can encompass a wide variety of activities. This book focuses on supervised or predictive modeling for text, using text data to make predictions about the world around us. We use the tidymodels framework for modeling, a consistent and flexible collection of R packages developed to encourage good statistical practice.
12.6 Machine Learning for Factor Investing
Guillaume Coqueret and Tony Guida
This book is intended to cover some advanced modelling techniques applied to equity investment strategies that are built on firm characteristics.
12.7 The caret Package
The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models.
This book is free online.
12.8 A Minimal rTorch Book
Alfonso R. Reyes
Practically, you can do everything you could with PyTorch within the R ecosystem.
12.9 Tidy Modeling with R
Max Kuhn and Julia Silge
This book provides an introduction to how to use the tidymodels suite of packages to create models using a tidyverse approach and encourages good methodology and statistical practice throughout demonstrated using series of applied examples.
This book is free online (currently a work in progress).
12.10 mlr3 book
The mlr3 package and ecosystem provide a generic, object-oriented, and extensible framework for classification, regression, survival analysis, and other machine learning tasks for the R language. They do not implement any learners, but provide a unified interface to many existing learners in R.
The book is free and work in progress. https://mlr3book.mlr-org.com/