图书简介

I. Fundamental Concepts 1. Introduction - Why the Term Machine Learning? - Why do We Need Machine Learning? - How is this Book Different? - Definitions - Software - Datasets 2. The Principles of Machine Learning Research - Overview - Principle #1: Machine Learning is Not Just Lazy Induction - Principle #2: Orienting Our Goals Relative to Prediction, Explanation, and Description - Principle #3: Labeling a Study as Exploratory or Confirmatory is too Simplistic - Principle #4: Report Everything - Summary 3. The Practices of Machine Learning - Comparing Algorithms and Models - Model Fit - Bias-Variance Tradeoff - Resampling - Classification - Conclusion II. Algorithms for Univariate Outcomes 4. Regularized Regression - Linear Regression - Logistic Regression - Regularization - Rationale for Regularization - Alternative Forms of Regularization - Bayesian Regression - Summary 5. Decision Trees - Introduction - Decision Tree Algorithms - Miscellaneous Topics 6. Ensembles - Bagging - Random Forests - Gradient Boosting - Interpretation - Empirical Example - Important Notes - Summary III. Algorithms for Multivariate Outcomes 7. Machine Learning and Measurement - Defining Measurement Error - Impact of Measurement Error - Assessing Measurement Error - Weighting - Alternative Methods - Summary 8. Machine Learning and Structural Equation Modeling - Latent Variables as Predictors - Predicting Latent Variables - Using Latent Variables as Outcomes and Predictors - Can Regularization Improve Generalizability in SEM? - Nonlinear Relationships and Latent Variables - Summary 9. Machine Learning with Mixed-Effects Models - Mixed-Effects Mod

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