Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
Credibility: Evaluating what’s been learned
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- Bu səhifədəki naviqasiya:
- Other estimates 151 Leave-one-out 151 The bootstrap 152 5.5
- Implementations: Real machine learning schemes 187 6.1 Decision trees 189
5 Credibility: Evaluating what’s been learned 143 5.1 Training and testing 144 5.2 Predicting performance 146 5.3 Cross-validation 149 5.4 Other estimates 151 Leave-one-out 151 The bootstrap 152 5.5 Comparing data mining methods 153 5.6 Predicting probabilities 157 Quadratic loss function 158 Informational loss function 159 Discussion 160 5.7 Counting the cost 161 Cost-sensitive classification 164 Cost-sensitive learning 165 Lift charts 166 ROC curves 168 Recall–precision curves 171 Discussion 172 Cost curves 173 5.8 Evaluating numeric prediction 176 5.9 The minimum description length principle 179 5.10 Applying the MDL principle to clustering 183 5.11 Further reading 184 6 Implementations: Real machine learning schemes 187 6.1 Decision trees 189 Numeric attributes 189 Missing values 191 Pruning 192 Estimating error rates 193 Complexity of decision tree induction 196 From trees to rules 198 C4.5: Choices and options 198 Discussion 199 6.2 Classification rules 200 Criteria for choosing tests 200 Missing values, numeric attributes 201 x C O N T E N TS P088407-FM.qxd 4/30/05 10:55 AM Page x Generating good rules 202 Using global optimization 205 Obtaining rules from partial decision trees 207 Rules with exceptions 210 Discussion 213 6.3 Extending linear models 214 The maximum margin hyperplane 215 Nonlinear class boundaries 217 Support vector regression 219 The kernel perceptron 222 Multilayer perceptrons 223 Discussion 235 6.4 Instance-based learning 235 Reducing the number of exemplars 236 Pruning noisy exemplars 236 Weighting attributes 237 Generalizing exemplars 238 Distance functions for generalized exemplars 239 Generalized distance functions 241 Discussion 242 6.5 Numeric prediction 243 Model trees 244 Building the tree 245 Pruning the tree 245 Nominal attributes 246 Missing values 246 Pseudocode for model tree induction 247 Rules from model trees 250 Locally weighted linear regression 251 Discussion 253 6.6 Clustering 254 Choosing the number of clusters 254 Incremental clustering 255 Category utility 260 Probability-based clustering 262 The EM algorithm 265 Extending the mixture model 266 Bayesian clustering 268 Discussion 270 6.7 Bayesian networks 271 Making predictions 272 Learning Bayesian networks 276 C O N T E N TS x i P088407-FM.qxd 4/30/05 10:55 AM Page xi Specific algorithms 278 Data structures for fast learning 280 Discussion 283 7 Transformations: Engineering the input and output 285 7.1 Attribute selection 288 Scheme-independent selection 290 Searching the attribute space 292 Scheme-specific selection 294 7.2 Discretizing numeric attributes 296 Unsupervised discretization 297 Entropy-based discretization 298 Other discretization methods 302 Entropy-based versus error-based discretization 302 Converting discrete to numeric attributes 304 7.3 Some useful transformations 305 Principal components analysis 306 Random projections 309 Text to attribute vectors 309 Time series 311 7.4 Automatic data cleansing 312 Improving decision trees 312 Robust regression 313 Detecting anomalies 314 7.5 Combining multiple models 315 Bagging 316 Bagging with costs 319 Randomization 320 Boosting 321 Additive regression 325 Additive logistic regression 327 Option trees 328 Logistic model trees 331 Stacking 332 Error-correcting output codes 334 7.6 Using unlabeled data 337 Clustering for classification 337 Co-training 339 EM and co-training 340 7.7 Further reading 341 x i i
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