Preface Acknowledgements About the Authors Chapter 1 Introduction to Machine Learning Chapter 2 Overview of Machine Learning in Microbiome Research Chapter 3 Accessing Model Accuracy and Goodness of Fit Tests for Normality Chapter 4 Overfitting and Underfitting Chapter 5 Assessing Model Accuracy Using Cross-Validation Chapter 6 Feature Engineering and Model Selection Chapter 7 Logistic Regression Chapter 8 Support Vector Machines Chapter 9 Classification Trees Chapter 10 Random Forest Chapter 11 The Evolution of Tree-Based Algorithms Chapter 12 Extreme Gradient Boosting (XGBoost) Chapter 13 Artificial Neural Networks and Deep Learning Chapter 14 Machine Learning Microbiome with SIAMCAT Chapter 15 Basic Performance Metrics for Machine Learning Models Chapter 16 Matthews Correlation Coefficient Chapter 17 Area Under the Receiver Operating Characteristic Curve (AUC-ROC) Chapter 18 Area Under the Precision-Recall Curve (AUC-PR) Chapter 19 Comparisons of Machine Learning Classification Models with Tidymodels