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Machine Learning The Art and Science of Algorithms that Make Sense of Data
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详细说明： Contents Preface xv Prologue: A machine learning sampler 1 1 The ingredients of machine learning 13 1.1 Tasks: the problems that can be solved with machine learning . . . . . . . 14 Looking for structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Evaluating performance on a task . . . . . . . . . . . . . . . . . . . . . . . . 18 1.2 Models: the output of machine learning . . . . . . . . . . . . . . . . . . . . 20 Geometric models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Probabili stic models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Logical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Grouping and grading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 1.3 Features: the workhorses of machine learning . . . . . . . . . . . . . . . . 38 Two uses of features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Feature construction and transformation . . . . . . . . . . . . . . . . . . . 41 Interaction between features . . . . . . . . . . . . . . . . . . . . . . . . . . 44 1.4 Summary and outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 What you’ll find in the rest of the book . . . . . . . . . . . . . . . . . . . . . 48 2 Binary classification and related tasks 49 2.1 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 ixx Contents Assessing classification performance . . . . . . . . . . . . . . . . . . . . . . 53 Visualising classification performance . . . . . . . . . . . . . . . . . . . . . 58 2.2 Scoring and ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Assessing and visualising ranking performance . . . . . . . . . . . . . . . . 63 Turning rankers into classifiers . . . . . . . . . . . . . . . . . . . . . . . . . 69 2.3 Class probability estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Assessing class probability estimates . . . . . . . . . . . . . . . . . . . . . . 73 Turning rankers into class probability estimators . . . . . . . . . . . . . . . 76 2.4 Binary classification and related tasks: Summary and further reading . . 79 3 Beyond binary classification 81 3.1 Handling more than two classes . . . . . . . . . . . . . . . . . . . . . . . . . 81 Multi-class classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Multi-class scores and probabilities . . . . . . . . . . . . . . . . . . . . . . 86 3.2 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 3.3 Unsupervised and descriptive learning . . . . . . . . . . . . . . . . . . . . 95 Predictive and descriptive clustering . . . . . . . . . . . . . . . . . . . . . . 96 Other descriptive models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 3.4 Beyond binary classification: Summary and further reading . . . . . . . . 102 4 Concept learning 104 4.1 The hypothesis space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 Least general generalisation . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 Internal disjunction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 4.2 Paths through the hypothesis space . . . . . . . . . . . . . . . . . . . . . . 112 Most general consistent hypotheses . . . . . . . . . . . . . . . . . . . . . . 116 Closed concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 4.3 Beyond conjunctive concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Using first-order logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 4.4 Learnability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 4.5 Concept learning: Summary and further reading . . . . . . . . . . . . . . . 127 5 Tree models 129 5.1 Decision trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 5.2 Ranking and probability estimation trees . . . . . . . . . . . . . . . . . . . 138 Sensitivity to skewed class distributions . . . . . . . . . . . . . . . . . . . . 143 5.3 Tree learning as variance reduction . . . . . . . . . . . . . . . . . . . . . . . 148 Regression trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148Preface xv Prologue: A machine learning sampler 1 1 The ingredients of machine learning 13 2 Binary classification and related tasks 49 3 Beyond binary classification 81 4 Concept learning 104 5 Tree models 129 6 Rule models 157 7 Linear models 194 8 Distance-based models 231 9 Probabilistic models 262 10 Features 298 11 Model ensembles 330 12 Machine learning experiments 343 Epilogue: Where to go from here 360 Important points to remember 363 References 367 Index 383 viiContents xi Clustering trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 5.4 Tree models: Summary and further reading . . . . . . . . . . . . . . . . . . 155 6 Rule models 157 6.1 Learning ordered rule lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Rule lists for ranking and probability estimation . . . . . . . . . . . . . . . 164 6.2 Learning unordered rule sets . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Rule sets for ranking and probability estimation . . . . . . . . . . . . . . . 173 A closer look at rule overlap . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 6.3 Descriptive rule learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 Rule learning for subgroup discovery . . . . . . . . . . . . . . . . . . . . . . 178 Association rule mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 6.4 First-order rule learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 6.5 Rule models: Summary and further reading . . . . . . . . . . . . . . . . . . 192 7 Linear models 194 7.1 The least-squares method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 Multivariate linear regression . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Regularised regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 Using least-squares regression for classification . . . . . . . . . . . . . . . 205 7.2 The perceptron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 7.3 Support vector machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Soft margin SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 7.4 Obtaining probabilities from linear classifiers . . . . . . . . . . . . . . . . 219 7.5 Going beyond linearity with kernel methods . . . . . . . . . . . . . . . . . 224 7.6 Linear models: Summary and further reading . . . . . . . . . . . . . . . . 228 8 Distance-based models 231 8.1 So many roads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 8.2 Neighbours and exemplars . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 8.3 Nearest-neighbour classification . . . . . . . . . . . . . . . . . . . . . . . . 242 8.4 Distance-based clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 K -means algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Clustering around medoids . . . . . . . . . . . . . . . . . . . . . . . . . . . 250 Silhouettes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 8.5 Hierarchical clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 8.6 From kernels to distances . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 8.7 Distance-based models: Summary and further reading . . . . . . . . . . . 260xii Contents 9 Probabilistic models 262 9.1 The normal distribution and its geometric interpretations . . . . . . . . . 266 9.2 Probabilistic models for categorical data . . . . . . . . . . . . . . . . . . . . 273 Using a naive Bayes model for classification . . . . . . . . . . . . . . . . . . 275 Training a naive Bayes model . . . . . . . . . . . . . . . . . . . . . . . . . . 279 9.3 Discriminative learning by optimising conditional likelihood . . . . . . . 282 9.4 Probabilistic models with hidden variables . . . . . . . . . . . . . . . . . . 286 Expectation-Maximisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 Gaussian mixture models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 9.5 Compression-based models . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 9.6 Probabilistic models: Summary and further reading . . . . . . . . . . . . . 295 10 Features 298 10.1 Kinds of feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Calculations on features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Categorical, ordinal and quantitative features . . . . . . . . . . . . . . . . 304 Structured features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 10.2 Feature transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Thresholding and discretisation . . . . . . . . . . . . . . . . . . . . . . . . . 308 Normalisation and calibration . . . . . . . . . . . . . . . . . . . . . . . . . . 314 Incomplete features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 10.3 Feature construction and selection . . . . . . . . . . . . . . . . . . . . . . . 322 Matrix transformations and decompositions . . . . . . . . . . . . . . . . . 324 10.4 Features: Summary and further reading . . . . . . . . . . . . . . . . . . . . 327 11 Model ensembles 330 11.1 Bagging and random forests . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 11.2 Boosting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 Boosted rule learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 11.3 Mapping the ensemble landscape . . . . . . . . . . . . . . . . . . . . . . . 338 Bias, variance and margins . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 Other ensemble methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Meta-learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 11.4 Model ensembles: Summary and further reading . . . . . . . . . . . . . . 341 12 Machine learning experiments 343 12.1 What to measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 12.2 How to measure it . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348Contents xiii 12.3 How to interpret it . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Interpretation of results over multiple data sets . . . . . . . . . . . . . . . . 354 12.4 Machine learning experiments: Summary and further reading . . . . . . . 357 Epilogue: Where to go from here 360 Important points to remember 363 References 367 Index 383 ...展开收缩
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