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10.8 ATTRIBUTE SELECTION 425with one of the single-attribute evaluators in the lower part of Table 10.9—notan attribute subset evaluator. Ranker not only ranks attributes but also performsattribute selection by removing the lower-ranking ones. You can set a cutoffthreshold below which attributes are discarded, or specify how many attributesto retain. You can specify certain attributes that must be retained regardless oftheir rank.
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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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Page 438 and 439: 10.4 LEARNING ALGORITHMS 405Table 1
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Page 444 and 445: 10.4 LEARNING ALGORITHMS 411Neural
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Page 448 and 449: 10.5 METALEARNING ALGORITHMS 415Tab
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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
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Page 462 and 463: 11.1 GETTING STARTED 429(a)(b)Figur
Page 464 and 465: 11.3 CONFIGURING AND CONNECTING THE
Page 466 and 467: 11.4 INCREMENTAL LEARNING 433pervis
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Page 471 and 472: 438 CHAPTER 12 | THE EXPERIMENTER12
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Page 477 and 478: 444 CHAPTER 12 | THE EXPERIMENTER(a
Page 479 and 480: 446 CHAPTER 12 | THE EXPERIMENTERIt
Page 482 and 483: chapter 13The Command-line Interfac
Page 484 and 485: 13.2 THE STRUCTURE OF WEKA 451Large
Page 486 and 487: The weka.classifiers packageThe cla
Page 488 and 489: 13.2 THE STRUCTURE OF WEKA 455Figur
Page 490 and 491: 13.3 COMMAND-LINE OPTIONS 457Table
Page 492: 13.3 COMMAND-LINE OPTIONS 459proced
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
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15.1 AN EXAMPLE CLASSIFIER 475m_Cla
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15.1 AN EXAMPLE CLASSIFIER 477* @re
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15.1 AN EXAMPLE CLASSIFIER 479** @p
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15.1 AN EXAMPLE CLASSIFIER 481for b
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15.2 CONVENTIONS FOR IMPLEMENTING C
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486 REFERENCESAsmis, E. 1984. Epicu
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488 REFERENCESCavnar, W. B., and J.
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490 REFERENCESDrucker, H. 1997. Imp
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492 REFERENCESFreund, Y., and L. Ma
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494 REFERENCESHall, M., G. Holmes,
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496 REFERENCESKohavi, R. 1995a. A s
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498 REFERENCESMann, T. 1993. Librar
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500 REFERENCESHeckerman, H. Mannila
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502 REFERENCESStone, P., and M. Vel
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IndexAactivation function, 234acuit
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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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