Data Mining: Practical Machine Learning Tools and ... - LIDeCC

More documents

Recommendations

Info

10.1 GETTING STARTED 375=== Run information ===Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2Relation: weatherInstances: 14Attributes: 5outlooktemperaturehumiditywindyplayTest mode: 10-fold cross-validation=== Classifier model (full training set) ===J48 pruned tree------------------outlook = sunny| humidity 75: no (3.0)outlook = overcast: yes (4.0)outlook = rainy| windy = TRUE: no (2.0)| windy = FALSE: yes (3.0)Number of Leaves : 5Size of the tree : 8Time taken to build model: 0.27 seconds=== Stratified cross-validation ====== Summary ===Correctly Classified Instances 9 64.2857 %Incorrectly Classified Instances 5 35.7143 %Kappa statistic 0.186Mean absolute error 0.2857Root mean squared error 0.4818Relative absolute error 60 %Root relative squared error 97.6586 %Total Number of Instances 14Figure 10.5 Output from the J4.8 decision tree learner.
376 CHAPTER 10 | THE EXPLORER=== Detailed Accuracy By Class ===TP Rate FP Rate Precision Recall F-Measure Class0.778 0.6 0.7 0.778 0.737 yes0.4 0.222 0.5 0.4 0.444 no=== Confusion Matrix ===a b
Page 2:
Data MiningPractical Machine Learni
Page 5 and 6:
Publisher:Publishing Services Manag
Page 7 and 8:
viFOREWORDThis book presents this n
Page 10 and 11:
CONTENTSix4 Algorithms: The basic m
Page 12 and 13:
CONTENTSxiGenerating good rules 202
Page 14 and 15:
CONTENTSxiii8 Moving on: Extensions
Page 16:
CONTENTSxv13 The command-line inter
Page 19 and 20:
xviiiLIST OF FIGURESFigure 4.10 The
Page 21 and 22:
xxLIST OF FIGURESFigure 10.13 Worki
Page 23 and 24:
xxiiLIST OF TABLESTable 5.2 Confide
Page 25 and 26:
xxivPREFACEalchemy. Instead, there
Page 27 and 28:
xxviPREFACEwho interprets them, and
Page 29 and 30:
xxviiiPREFACEin Section 6.3. We hav
Page 31 and 32:
xxxPREFACEration. All who have work
Page 34:
partIMachine Learning Toolsand Tech
Page 37 and 38:
4 CHAPTER 1 | WHAT’S IT ALL ABOUT
Page 39 and 40:
Page 41 and 42:
Page 43 and 44:
10 CHAPTER 1 | WHAT’S IT ALL ABOU
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
Page 71 and 72:
Page 74 and 75:
chapter 2Input:Concepts, Instances,
Page 76 and 77:
2.1 WHAT’S A CONCEPT? 43increase
Page 78 and 79:
2.2 WHAT’S IN AN EXAMPLE? 45data
Page 80 and 81:
2.2 WHAT’S IN AN EXAMPLE? 47Table
Page 82 and 83:
2.3 WHAT’S IN AN ATTRIBUTE? 49Tab
Page 84 and 85:
2.3 WHAT’S IN AN ATTRIBUTE? 51Not
Page 86 and 87:
2.4 PREPARING THE INPUT 53cleaned u
Page 88 and 89:
missing values in this dataset). Th
Page 90 and 91:
dividing by the range between the m
Page 92 and 93:
2.4 PREPARING THE INPUT 59a record
Page 94 and 95:
chapter 3Output:Knowledge Represent
Page 96 and 97:
3.2 DECISION TREES 63Alternatively,
Page 98 and 99:
3.3 CLASSIFICATION RULES 65nated by
Page 100 and 101:
3.3 CLASSIFICATION RULES 671abx = 1
Page 102 and 103:
3.4 ASSOCIATION RULES 69yes, then i
Page 104 and 105:
3.5 RULES WITH EXCEPTIONS 71If peta
Page 106 and 107:
3.6 RULES INVOLVING RELATIONS 73ica
Page 108 and 109:
Standard relations include equality
Page 110 and 111:
PRP =-56.1+0.049 MYCT+0.015 MMIN+0.
Page 112 and 113:
3.8 INSTANCE-BASED REPRESENTATION 7
Page 114 and 115:
3.9 Clusters3.9 CLUSTERS 81When clu
Page 116 and 117:
chapter 4Algorithms:The Basic Metho
Page 118 and 119:
4.1 INFERRING RUDIMENTARY RULES 85F
Page 120 and 121:
described overfitting-avoidance bia
Page 122 and 123:
4.2 STATISTICAL MODELING 89Table 4.
Page 124 and 125:
just as we calculated previously. A
Page 126 and 127:
4.2 STATISTICAL MODELING 93Table 4.
Page 128 and 129:
of a document. Instead, a document
Page 130 and 131:
4.3 DIVIDE-AND-CONQUER: CONSTRUCTIN
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
4.4 COVERING ALGORITHMS: CONSTRUCTI
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
4.5 MINING ASSOCIATION RULES 113acc
Page 148 and 149:
4.5 MINING ASSOCIATION RULES 115Tab
Page 150 and 151:
4.5 MINING ASSOCIATION RULES 117whi
Page 152 and 153:
4.6 LINEAR MODELS 119through the da
Page 154 and 155:
4.6 LINEAR MODELS 121However, linea
Page 156 and 157:
4.6 LINEAR MODELS 123n( i)i iÂ 1-x
Page 158 and 159:
4.6 LINEAR MODELS 125Set all weight
Page 160 and 161:
4.6 LINEAR MODELS 127While some ins
Page 162 and 163:
4.7 INSTANCE-BASED LEARNING 129When
Page 164 and 165:
4.7 INSTANCE-BASED LEARNING 131Figu
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
4.8 CLUSTERING 137As we saw in Sect
Page 172 and 173:
4.9 FURTHER READING 139can be updat
Page 174 and 175:
4.9 FURTHER READING 141Bayes was an
Page 176 and 177:
chapter 5Credibility:Evaluating Wha
Page 178 and 179:
5.1 TRAINING AND TESTING 145of each
Page 180 and 181:
ather than error rate, so this corr
Page 182 and 183:
5.3 CROSS-VALIDATION 149mediate con
Page 184 and 185:
5.4 OTHER ESTIMATES 151A single 10-
Page 186 and 187:
90% used in 10-fold cross-validatio
Page 188 and 189:
5.5 COMPARING DATA MINING METHODS 1
Page 190 and 191:
5.6 PREDICTING PROBABILITIES 157In
Page 192 and 193:
5.6 PREDICTING PROBABILITIES 159whe
Page 194 and 195:
mental job expected of a loss funct
Page 196 and 197:
y the total number of positives, wh
Page 198 and 199:
5.7 COUNTING THE COST 165different
Page 200 and 201:
5.7 COUNTING THE COST 167Table 5.6D
Page 202 and 203:
5.7 COUNTING THE COST 169100%80%tru
Page 204 and 205:
5.7 COUNTING THE COST 171should cho
Page 206 and 207:
Different terms are used in differe
Page 208 and 209:
5.7 COUNTING THE COST 1750.5Anormal
Page 210 and 211:
5.8 EVALUATING NUMERIC PREDICTION 1
Page 212 and 213:
5.9 THE MINIMUM DESCRIPTION LENGTH
Page 214 and 215:
5.9 THE MINIMUM DESCRIPTION LENGTH
Page 216 and 217:
5.10 APPLYING THE MDL PRINCIPLE TO
Page 218:
5.11 FURTHER READING 185tion theory
Page 221 and 222:
188 CHAPTER 6 | IMPLEMENTATIONS: RE
Page 223 and 224:
Page 225 and 226:
Page 227 and 228:
Page 229 and 230:
Page 231 and 232:
Page 233 and 234:
Page 235 and 236:
Page 237 and 238:
Page 239:
Page 243 and 244:
Page 245 and 246:
Page 247 and 248:
Page 249 and 250:
Page 251 and 252:
Page 253 and 254:
Page 255 and 256:
Page 257 and 258:
Page 259 and 260:
Page 261 and 262:
Page 263 and 264:
Page 265 and 266:
Page 267 and 268:
Page 269 and 270:
Page 271 and 272:
Page 273 and 274:
Page 275 and 276:
Page 277 and 278:
Page 279 and 280:
Page 281 and 282:
Page 283 and 284:
Page 285 and 286:
Page 287 and 288:
Page 289 and 290:
Page 291 and 292:
Page 293 and 294:
Page 295 and 296:
Page 297 and 298:
Page 299 and 300:
Page 301 and 302:
Page 303 and 304:
Page 305 and 306:
Page 307 and 308:
Page 309 and 310:
Page 311 and 312:
Page 313 and 314:
Page 315 and 316:
Page 318 and 319:
chapter 7Transformations:Engineerin
Page 320 and 321:
7.1 ATTRIBUTE SELECTION 287attribut
Page 322 and 323:
7.1 ATTRIBUTE SELECTION 289and less
Page 324 and 325:
tion—and it is much easier to und
Page 326 and 327:
7.1 ATTRIBUTE SELECTION 293outlook
Page 328 and 329:
7.1 ATTRIBUTE SELECTION 295the t-te
Page 330 and 331:
7.2 DISCRETIZING NUMERIC ATTRIBUTES
Page 332 and 333:
Page 334 and 335:
Page 336 and 337:
Page 338 and 339:
7.3 SOME USEFUL TRANSFORMATIONS 305
Page 340 and 341:
Page 342 and 343:
Page 344 and 345:
Page 346 and 347:
7.4 AUTOMATIC DATA CLEANSING 313Int
Page 348 and 349:
7.5 COMBINING MULTIPLE MODELS 315da
Page 350 and 351:
7.5 COMBINING MULTIPLE MODELS 317pa
Page 352 and 353:
7.5 COMBINING MULTIPLE MODELS 319mo
Page 354 and 355:
7.5 COMBINING MULTIPLE MODELS 321Ra
Page 356 and 357:
7.5 COMBINING MULTIPLE MODELS 323Ho
Page 358 and 359: 7.5 COMBINING MULTIPLE MODELS 325Th
Page 360 and 361: 7.5 COMBINING MULTIPLE MODELS 327of
Page 362 and 363: 7.5 COMBINING MULTIPLE MODELS 329ou
Page 364 and 365: 7.5 COMBINING MULTIPLE MODELS 331ev
Page 366 and 367: 7.5 COMBINING MULTIPLE MODELS 333be
Page 368 and 369: 7.5 COMBINING MULTIPLE MODELS 335Ta
Page 370 and 371: 7.6 Using unlabeled data7.6 USING U
Page 372 and 373: 7.6 USING UNLABELED DATA 339automat
Page 374 and 375: 7.7 FURTHER READING 341deal with we
Page 376: 7.7 FURTHER READING 343Domingos (19
Page 379 and 380: 346 CHAPTER 8 | MOVING ON: EXTENSIO
Page 398 and 399: chapter 9Introduction to WekaExperi
Page 400 and 401: 9.2 How do you use it?9.2 HOW DO YO
Page 402 and 403: chapter 10The ExplorerWeka’s main
Page 404 and 405: 10.1 GETTING STARTED 371(a)(b)(c)Fi
Page 406 and 407: 10.1 GETTING STARTED 373deviation.
Page 410 and 411: 10.1 GETTING STARTED 377Doing it ag
Page 412 and 413: 10.1 GETTING STARTED 379(a)(b)Figur
Page 414 and 415: 10.2 EXPLORING THE EXPLORER 381(a)(
Page 416 and 417: 10.2 EXPLORING THE EXPLORER 383(b)(
Page 418 and 419: 10.2 EXPLORING THE EXPLORER 385(a)(
Page 420 and 421: 10.2 EXPLORING THE EXPLORER 387+ 0.
Page 422 and 423: 10.2 EXPLORING THE EXPLORER 389posi
Page 424 and 425: 10.2 EXPLORING THE EXPLORER 391Figu
Page 426 and 427: 10.3 FILTERING ALGORITHMS 393method
Page 428 and 429: 10.3 FILTERING ALGORITHMS 395(b)Fig
Page 430 and 431: 10.3 FILTERING ALGORITHMS 397or fir
Page 432 and 433: 10.3 FILTERING ALGORITHMS 399attrib
Page 434 and 435: 10.3 FILTERING ALGORITHMS 401it int
Page 436 and 437: 10.4 LEARNING ALGORITHMS 403There i
Page 438 and 439: 10.4 LEARNING ALGORITHMS 405Table 1
Page 440 and 441: 10.4 LEARNING ALGORITHMS 407Figure
Page 442 and 443: 10.4 LEARNING ALGORITHMS 409value (
Page 444 and 445: 10.4 LEARNING ALGORITHMS 411Neural
Page 446 and 447: 10.4 LEARNING ALGORITHMS 413there a
Page 448 and 449: 10.5 METALEARNING ALGORITHMS 415Tab
Page 450 and 451: 10.5 METALEARNING ALGORITHMS 417Com
Page 452 and 453: 10.7 ASSOCIATION-RULE LEARNERS 419N
Page 454 and 455: 10.8 ATTRIBUTE SELECTION 421Table 1
Page 456 and 457: 10.8 ATTRIBUTE SELECTION 423tribute
Page 458:
10.8 ATTRIBUTE SELECTION 425with on
Page 461 and 462:
428 CHAPTER 11 | THE KNOWLEDGE FLOW
Page 463 and 464:
Page 465 and 466:
Page 467 and 468:
Page 470 and 471:
chapter 12The ExperimenterThe Explo
Page 472 and 473:
12.1 GETTING STARTED 439Dataset,Run
Page 474 and 475:
12.2 SIMPLE SETUP 441cance test of
Page 476 and 477:
12.4 THE ANALYZE PANEL 443advanced
Page 478 and 479:
12.5 DISTRIBUTING PROCESSING OVER S
Page 480:
12.5 DISTRIBUTING PROCESSING OVER S
Page 483 and 484:
450 CHAPTER 13 | THE COMMAND-LINE I
Page 485 and 486:
Page 487 and 488:
Page 489 and 490:
Page 491 and 492:
Page 494 and 495:
chapter 14Embedded Machine Learning
Page 496 and 497:
14.2 GOING THROUGH THE CODE 463/***
Page 498 and 499:
14.2 GOING THROUGH THE CODE 465// I
Page 500 and 501:
14.2 GOING THROUGH THE CODE 467}//
Page 502:
14.2 GOING THROUGH THE CODE 469filt
Page 505 and 506:
472 CHAPTER 15 | WRITING NEW LEARNI
Page 507 and 508:
Page 509 and 510:
Page 511 and 512:
Page 513 and 514:
Page 515 and 516:
Page 518 and 519:
ReferencesAdriaans, P., and D. Zant
Page 520 and 521:
Bouckaert, R. R. 2004. Bayesian net
Page 522 and 523:
REFERENCES 489Cypher, A., editor. 1
Page 524 and 525:
REFERENCES 491Fix, E., and J. L. Ho
Page 526 and 527:
REFERENCES 493Gennari, J. H., P. La
Page 528 and 529:
Conference on Knowledge Discovery a
Page 530 and 531:
REFERENCES 497Kushmerick, N., D. S.
Page 532 and 533:
Moore, A. W., and M. S. Lee. 1994.
Page 534 and 535:
REFERENCES 501editor, Proceedings o
Page 536:
REFERENCES 503Webb, G. I., J. Bough
Page 539 and 540:
506 INDEXanomaly detection systems,
Page 541 and 542:
508 INDEXcausal relations, 350CfsSu
Page 543 and 544:
510 INDEXCSVLoader, 381cumulative m
Page 545 and 546:
512 INDEXExperimenter, 437-447advan
Page 547 and 548:
514 INDEXimplementation—real-worl
Page 549 and 550:
516 INDEXlistOptions(), 482literary
Page 551 and 552:
518 INDEXnumeric prediction (contin
Page 553 and 554:
520 INDEXrelational data, 49relatio
Page 555 and 556:
522 INDEXsupport vector, 216support
Page 557 and 558:
524 INDEXWinnow, 410Winnow algorith
show all

Data Mining: Practical Machine Learning Tools and ... - LIDeCC

Create successful ePaper yourself

Delete template?

Save as template?