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13.3 COMMAND-LINE OPTIONS 459procedure, reduced-error pruning (Section 6.2, pages 202–203) can be performed.The -N option governs the size of the holdout set: the dataset is dividedequally into that number of parts and the last is held out (default value 3). Youcan smooth the probability estimates using the Laplace technique, set therandom number seed for shuffling the data when selecting a pruning set, andstore the instance information for future visualization. Finally, to build a binarytree instead of one with multiway branches for nominal attributes, use -B.
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Data MiningPractical Machine Learni
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Publisher:Publishing Services Manag
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viFOREWORDThis book presents this n
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CONTENTSix4 Algorithms: The basic m
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CONTENTSxiGenerating good rules 202
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CONTENTSxiii8 Moving on: Extensions
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CONTENTSxv13 The command-line inter
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xviiiLIST OF FIGURESFigure 4.10 The
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xxLIST OF FIGURESFigure 10.13 Worki
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xxiiLIST OF TABLESTable 5.2 Confide
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xxivPREFACEalchemy. Instead, there
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xxviPREFACEwho interprets them, and
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xxviiiPREFACEin Section 6.3. We hav
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xxxPREFACEration. All who have work
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partIMachine Learning Toolsand Tech
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4 CHAPTER 1 | WHAT’S IT ALL ABOUT
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10 CHAPTER 1 | WHAT’S IT ALL ABOU
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chapter 2Input:Concepts, Instances,
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2.1 WHAT’S A CONCEPT? 43increase
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2.2 WHAT’S IN AN EXAMPLE? 45data
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2.2 WHAT’S IN AN EXAMPLE? 47Table
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2.3 WHAT’S IN AN ATTRIBUTE? 49Tab
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2.3 WHAT’S IN AN ATTRIBUTE? 51Not
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2.4 PREPARING THE INPUT 53cleaned u
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missing values in this dataset). Th
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dividing by the range between the m
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2.4 PREPARING THE INPUT 59a record
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chapter 3Output:Knowledge Represent
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3.2 DECISION TREES 63Alternatively,
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3.3 CLASSIFICATION RULES 65nated by
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3.3 CLASSIFICATION RULES 671abx = 1
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3.4 ASSOCIATION RULES 69yes, then i
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3.5 RULES WITH EXCEPTIONS 71If peta
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3.6 RULES INVOLVING RELATIONS 73ica
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Standard relations include equality
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PRP =-56.1+0.049 MYCT+0.015 MMIN+0.
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3.8 INSTANCE-BASED REPRESENTATION 7
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3.9 Clusters3.9 CLUSTERS 81When clu
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chapter 4Algorithms:The Basic Metho
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4.1 INFERRING RUDIMENTARY RULES 85F
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described overfitting-avoidance bia
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4.2 STATISTICAL MODELING 89Table 4.
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just as we calculated previously. A
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4.2 STATISTICAL MODELING 93Table 4.
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of a document. Instead, a document
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4.3 DIVIDE-AND-CONQUER: CONSTRUCTIN
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4.4 COVERING ALGORITHMS: CONSTRUCTI
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4.5 MINING ASSOCIATION RULES 113acc
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4.5 MINING ASSOCIATION RULES 115Tab
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4.5 MINING ASSOCIATION RULES 117whi
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4.6 LINEAR MODELS 119through the da
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4.6 LINEAR MODELS 121However, linea
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4.6 LINEAR MODELS 123n( i)i iÂ 1-x
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4.6 LINEAR MODELS 125Set all weight
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4.6 LINEAR MODELS 127While some ins
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4.7 INSTANCE-BASED LEARNING 129When
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4.7 INSTANCE-BASED LEARNING 131Figu
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4.8 CLUSTERING 137As we saw in Sect
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4.9 FURTHER READING 139can be updat
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4.9 FURTHER READING 141Bayes was an
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chapter 5Credibility:Evaluating Wha
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5.1 TRAINING AND TESTING 145of each
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ather than error rate, so this corr
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5.3 CROSS-VALIDATION 149mediate con
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5.4 OTHER ESTIMATES 151A single 10-
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90% used in 10-fold cross-validatio
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5.5 COMPARING DATA MINING METHODS 1
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5.6 PREDICTING PROBABILITIES 157In
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5.6 PREDICTING PROBABILITIES 159whe
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mental job expected of a loss funct
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y the total number of positives, wh
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5.7 COUNTING THE COST 165different
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5.7 COUNTING THE COST 167Table 5.6D
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5.7 COUNTING THE COST 169100%80%tru
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5.7 COUNTING THE COST 171should cho
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Different terms are used in differe
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5.7 COUNTING THE COST 1750.5Anormal
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5.8 EVALUATING NUMERIC PREDICTION 1
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5.9 THE MINIMUM DESCRIPTION LENGTH
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5.9 THE MINIMUM DESCRIPTION LENGTH
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5.10 APPLYING THE MDL PRINCIPLE TO
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5.11 FURTHER READING 185tion theory
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188 CHAPTER 6 | IMPLEMENTATIONS: RE
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chapter 7Transformations:Engineerin
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7.1 ATTRIBUTE SELECTION 287attribut
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7.1 ATTRIBUTE SELECTION 289and less
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tion—and it is much easier to und
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7.1 ATTRIBUTE SELECTION 293outlook
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7.1 ATTRIBUTE SELECTION 295the t-te
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7.2 DISCRETIZING NUMERIC ATTRIBUTES
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7.3 SOME USEFUL TRANSFORMATIONS 305
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7.4 AUTOMATIC DATA CLEANSING 313Int
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7.5 COMBINING MULTIPLE MODELS 315da
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7.5 COMBINING MULTIPLE MODELS 317pa
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7.5 COMBINING MULTIPLE MODELS 319mo
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7.5 COMBINING MULTIPLE MODELS 321Ra
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7.5 COMBINING MULTIPLE MODELS 323Ho
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7.5 COMBINING MULTIPLE MODELS 325Th
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7.5 COMBINING MULTIPLE MODELS 327of
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7.5 COMBINING MULTIPLE MODELS 329ou
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7.5 COMBINING MULTIPLE MODELS 331ev
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7.5 COMBINING MULTIPLE MODELS 333be
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7.5 COMBINING MULTIPLE MODELS 335Ta
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7.6 Using unlabeled data7.6 USING U
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7.6 USING UNLABELED DATA 339automat
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7.7 FURTHER READING 341deal with we
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7.7 FURTHER READING 343Domingos (19
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346 CHAPTER 8 | MOVING ON: EXTENSIO
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chapter 9Introduction to WekaExperi
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9.2 How do you use it?9.2 HOW DO YO
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chapter 10The ExplorerWeka’s main
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10.1 GETTING STARTED 371(a)(b)(c)Fi
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10.1 GETTING STARTED 373deviation.
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10.1 GETTING STARTED 375=== Run inf
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10.1 GETTING STARTED 377Doing it ag
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10.1 GETTING STARTED 379(a)(b)Figur
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10.2 EXPLORING THE EXPLORER 381(a)(
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10.2 EXPLORING THE EXPLORER 383(b)(
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10.2 EXPLORING THE EXPLORER 385(a)(
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10.2 EXPLORING THE EXPLORER 387+ 0.
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10.2 EXPLORING THE EXPLORER 389posi
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10.2 EXPLORING THE EXPLORER 391Figu
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10.3 FILTERING ALGORITHMS 393method
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10.3 FILTERING ALGORITHMS 395(b)Fig
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10.3 FILTERING ALGORITHMS 397or fir
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10.3 FILTERING ALGORITHMS 399attrib
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10.3 FILTERING ALGORITHMS 401it int
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10.4 LEARNING ALGORITHMS 403There i
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10.4 LEARNING ALGORITHMS 405Table 1
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10.4 LEARNING ALGORITHMS 407Figure
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Page 444 and 445: 10.4 LEARNING ALGORITHMS 411Neural
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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 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 491: 458 CHAPTER 13 | THE COMMAND-LINE I
Page 495 and 496: 462 CHAPTER 14 | EMBEDDED MACHINE L
Page 504 and 505: chapter 15Writing New Learning Sche
Page 506 and 507: 15.1 AN EXAMPLE CLASSIFIER 473packa
Page 508 and 509: 15.1 AN EXAMPLE CLASSIFIER 475m_Cla
Page 510 and 511: 15.1 AN EXAMPLE CLASSIFIER 477* @re
Page 512 and 513: 15.1 AN EXAMPLE CLASSIFIER 479** @p
Page 514 and 515: 15.1 AN EXAMPLE CLASSIFIER 481for b
Page 516: 15.2 CONVENTIONS FOR IMPLEMENTING C
Page 519 and 520: 486 REFERENCESAsmis, E. 1984. Epicu
Page 521 and 522: 488 REFERENCESCavnar, W. B., and J.
Page 523 and 524: 490 REFERENCESDrucker, H. 1997. Imp
Page 525 and 526: 492 REFERENCESFreund, Y., and L. Ma
Page 527 and 528: 494 REFERENCESHall, M., G. Holmes,
Page 529 and 530: 496 REFERENCESKohavi, R. 1995a. A s
Page 531 and 532: 498 REFERENCESMann, T. 1993. Librar
Page 533 and 534: 500 REFERENCESHeckerman, H. Mannila
Page 535 and 536: 502 REFERENCESStone, P., and M. Vel
Page 538 and 539: IndexAactivation function, 234acuit
Page 540 and 541: INDEX 507automatic filtering, 315av
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INDEX 509randomization, 320-321stac
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INDEX 511document classification, 9
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INDEX 513Fisher, R. A., 15flat file
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INDEX 515KK2, 278Kappa statistic, 1
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INDEX 517multiclass alternating dec
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INDEX 519predicting performance, 14
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INDEX 521sigmoid kernel, 219Simple
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INDEX 523unlabeled data, 337-341clu
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About the AuthorsIan H. Witten is a
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