Suchergebnisse

Cover -- Half Title Page -- Series Page -- Title Page -- Copyright -- Series Editor's Note -- Preface -- Contents -- Part I. Fundamental Concepts -- 1. Introduction -- 1.1 Why the Term Machine Learning? -- 1.1.1 Why Not Just Call It Statistics? -- 1.2 Why Do We Need Machine Learning? -- 1.2.1 Machine Learning Thesis -- 1.3 How Is This Book Different? -- 1.3.1 Prerequisites for the Book -- 1.4 Definitions -- 1.4.1 Model vs. Algorithm -- 1.4.2 Prediction -- 1.5 Software -- 1.6 Datasets -- 1.6.1 Grit -- 1.6.2 National Survey on Drug Use and Health from 2014 -- 1.6.3 Early Childhood Learning Study-Kindergarten Cohort -- 1.6.4 Big Five Inventory -- 1.6.5 Holzinger-Swineford -- 1.6.6 PHE Exposure -- 1.6.7 Professor Ratings -- 2. The Principles of Machine Learning Research -- 2.1 Key Terminology -- 2.2 Overview -- 2.3 Principle #1: Machine Learning Is Not Just Lazy Induction -- 2.3.1 Complexity -- 2.3.2 Abduction -- 2.4 Principle #2: Orienting Our Goals Relative to Prediction, Explanation, and Description -- 2.5 Principle #3: Labeling a Study as Exploratory or Confirmatory Is Too Simplistic -- 2.5.1 Model Size -- 2.5.2 Level of Hypothesis -- 2.5.3 Example -- 2.5.4 Types of Relationships -- 2.5.5 Exploratory Data Analysis -- 2.6 Principle #4: Report Everything -- 2.7 Summary -- 2.7.1 Further Reading -- 3. The Practices of Machine Learning -- 3.1 Key Terminology -- 3.2 Comparing Algorithms and Models -- 3.3 Model Fit -- 3.3.1 Regression -- 3.4 Bias-Variance Trade-Off -- 3.5 Resampling -- 3.5.1 k-Fold CV -- 3.5.2 Nested CV -- 3.5.3 Bootstrap Sampling -- 3.5.4 Recommendations -- 3.6 Classification -- 3.6.1 Receiver Operating Characteristic (ROC) Curves -- 3.7 Imbalanced Outcomes -- 3.7.1 Sampling -- 3.8 Conclusion -- 3.8.1 Further Reading -- 3.8.2 Computational Time and Resources -- Part II. Algorithms for Univariate Outcomes -- 4. Regularized Regression.

"Over the past 20 years, there has been an incredible change in the size, structure, and types of data collected in the social and behavioral sciences. Thus, social and behavioral researchers have increasingly been asking the question: "What do I do with all of this data?" The goal of this book is to help answer that question. It is our viewpoint that in social and behavioral research, to answer the question "What do I do with all of this data?", one needs to know the latest advances in the algorithms and think deeply about the interplay of statistical algorithms, data, and theory. An important distinction between this book and most other books in the area of machine learning is our focus on theory"--

"Growth models are among the core methods for analyzing how and when people change. Discussing both structural equation and multilevel modeling approaches, this book leads readers step by step through applying each model to longitudinal data to answer particular research questions. It demonstrates cutting-edge ways to describe linear and nonlinear change patterns, examine within-person and between-person differences in change, study change in latent variables, identify leading and lagging indicators of change, evaluate co-occurring patterns of change across multiple variables, and more. User-friendly features include real data examples, code (for Mplus or NLMIXED in SAS, and OpenMx or nlme in R), discussion of the output, and interpretation of each model's results."--Publisher description