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chapter 13The Command-line InterfaceLurking behind Weka’s interactive interfaces—the Explorer, the KnowledgeFlow, and the Experimenter—lies its basic functionality. This can be accessed inraw form through a command-line interface. Select Simple CLI from the interfacechoices at the bottom of Figure 10.3(a) to bring up a plain textual panelwith a line at the bottom on which you enter commands. Alternatively, use theoperating system’s command-line interface to run the classes in weka.jardirectly, in which case you must first set the CLASSPATH environment variableas explained in Weka’s README file.13.1 Getting startedAt the beginning of Section 10.1 we used the Explorer to invoke the J4.8 learneron the weather data. To do the same thing in the command-line interface, typejava weka.classifiers.trees.J48 -t data/weather.arff449
450 CHAPTER 13 | THE COMMAND-LINE INTERFACEinto the line at the bottom of the text panel. This incantation calls the Javavirtual machine (in the Simple CLI, Java is already loaded) and instructs it toexecute J4.8. Weka is organized in packages that correspond to a directory hierarchy.The program to be executed is called J48 and resides in the trees package,which is a subpackage of classifiers, which is part of the overall weka package.The next section gives more details of the package structure. The -t optionsignals that the next argument is the name of the training file: we are assumingthat the weather data resides in a data subdirectory of the directory fromwhich you fired up Weka. The result resembles the text shown in Figure 10.5.In the Simple CLI it appears in the panel above the line where you typed thecommand.13.2 The structure of WekaWe have explained how to invoke filtering and learning schemes with theExplorer and connect them together with the Knowledge Flow interface. To gofurther, it is necessary to learn something about how Weka is put together.Detailed, up-to-date information can be found in the online documentationincluded in the distribution. This is more technical than the descriptions of thelearning and filtering schemes given by the More button in the Explorer andKnowledge Flow’s object editors. It is generated directly from comments in thesource code using Sun’s Javadoc utility. To understand its structure, you need toknow how Java programs are organized.Classes, instances, and packagesEvery Java program is implemented as a class. In object-oriented programming,a class is a collection of variables along with some methods that operate on them.Together, they define the behavior of an object belonging to the class. An objectis simply an instantiation of the class that has values assigned to all the class’svariables. In Java, an object is also called an instance of the class. Unfortunately,this conflicts with the terminology used in this book, where the terms class andinstance appear in the quite different context of machine learning. From nowon, you will have to infer the intended meaning of these terms from theircontext. This is not difficult—and sometimes we’ll use the word object insteadof Java’s instance to make things clear.In Weka, the implementation of a particular learning algorithm is encapsulatedin a class. For example, the J48 class described previously builds a C4.5decision tree. Each time the Java virtual machine executes J48, it creates aninstance of this class by allocating memory for building and storing a decisiontree classifier. The algorithm, the classifier it builds, and a procedure for outputtingthe classifier are all part of that instantiation of the J48 class.
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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 325Th
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7.5 COMBINING MULTIPLE MODELS 327of
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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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Page 456 and 457: 10.8 ATTRIBUTE SELECTION 423tribute
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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
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Page 485 and 486: 452 CHAPTER 13 | THE COMMAND-LINE I
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 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.
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Moore, A. W., and M. S. Lee. 1994.
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REFERENCES 501editor, Proceedings o
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REFERENCES 503Webb, G. I., J. Bough
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506 INDEXanomaly detection systems,
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508 INDEXcausal relations, 350CfsSu
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510 INDEXCSVLoader, 381cumulative m
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512 INDEXExperimenter, 437-447advan
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514 INDEXimplementation—real-worl
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516 INDEXlistOptions(), 482literary
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518 INDEXnumeric prediction (contin
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520 INDEXrelational data, 49relatio
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522 INDEXsupport vector, 216support
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524 INDEXWinnow, 410Winnow algorith
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