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Intro -- Preface -- Contents -- Part I Basic Concepts and Architecture -- 1 Introduction -- 1.1 Organization of the Book -- 1.2 Basic Concepts and Architecture (Part I) -- 1.3 Advanced Techniques and Methods (Part II) -- 1.4 Repositories of Experimental Results (Part III) -- References -- 2 Metalearning Approaches for Algorithm Selection I (Exploiting Rankings) -- 2.1 Introduction -- 2.2 Different Forms of Recommendation -- 2.3 Ranking Models for Algorithm Selection -- 2.4 Using a Combined Measure of Accuracy and Runtime -- 2.5 Extensions and Other Approaches -- References -- 3 Evaluating Recommendations of Metalearning/AutoML Systems -- 3.1 Introduction -- 3.2 Methodology for Evaluating Base-Level Algorithms -- 3.3 Normalization of Performance for Base-Level Algorithms -- 3.4 Methodology for Evaluating Metalearning and AutoML Systems -- 3.5 Evaluating Recommendations by Correlation -- 3.6 Evaluating the Effects of Recommendations -- 3.7 Some Useful Measures -- References -- 4 Dataset Characteristics (Metafeatures) -- 4.1 Introduction -- 4.2 Data Characterization Used in Classification Tasks -- 4.3 Data Characterization Used in Regression Tasks -- 4.4 Data Characterization Used in Time Series Tasks -- 4.5 Data Characterization Used in Clustering Tasks -- 4.6 Deriving New Features from the Basic Set -- 4.7 Selection of Metafeatures -- 4.8 Algorithm-Specific Characterization and Representation Issues -- 4.9 Establishing Similarity Between Datasets -- References -- 5 Metalearning Approaches for Algorithm Selection II -- 5.1 Introduction -- 5.2 Using Regression Models in Metalearning Systems -- 5.3 Using Classification at Meta-level for the Prediction of Applicability -- 5.4 Methods Based on Pairwise Comparisons -- 5.5 Pairwise Approach for a Set of Algorithms -- 5.6 Iterative Approach of Conducting Pairwise Tests -- 5.7 Using ART Trees and Forests.