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12.2 SIMPLE SETUP 441cance test of the performance of the first learning scheme (J48) versus that of theother two (OneR and ZeroR) will be displayed in the large panel on the right.We are comparing the percent correct statistic: this is selected by default asthe comparison field shown toward the left of Figure 12.2. The three methodsare displayed horizontally, numbered (1), (2), and (3), as the heading of a littletable. The labels for the columns are repeated at the bottom—trees.J48,rules.OneR, and rules.ZeroR—in case there is insufficient space for them in theheading. The inscrutable integers beside the scheme names identify whichversion of the scheme is being used. They are present by default to avoid confusionamong results generated using different versions of the algorithms. Thevalue in parentheses at the beginning of the iris row (100) is the number ofexperimental runs: 10 times 10-fold cross-validation.The percentage correct for the three schemes is shown in Figure 12.2: 94.73%for method 1, 93.53% for method 2, and 33.33% for method 3. The symbolplaced beside a result indicates that it is statistically better (v) or worse (*) thanthe baseline scheme—in this case J4.8—at the specified significance level (0.05,or 5%). The corrected resampled t-test from Section 5.5 (page 157) is used. Here,method 3 is significantly worse than method 1, because its success rate is followedby an asterisk. At the bottom of columns 2 and 3 are counts (x/y/z) of the numberof times the scheme was better than (x), the same as (y), or worse than (z) thebaseline scheme on the datasets used in the experiment. In this case there is onlyone dataset; method 2 was equivalent to method 1 (the baseline) once, andmethod 3 was worse than it once. (The annotation (v/ /*) is placed at the bottomof column 1 to help you remember the meanings of the three counts x/y/z.)12.2 Simple setupIn the Setup panel shown in Figure 12.1(a) we left most options at their defaultvalues. The experiment is a 10-fold cross-validation repeated 10 times. You canalter the number of folds in the box at center left and the number of repetitionsin the box at center right. The experiment type is classification; you can specifyregression instead. You can choose several datasets, in which case each algorithmis applied to each dataset, and change the order of iteration using the Data setsfirst and Algorithm first buttons. The alternative to cross-validation is theholdout method. There are two variants, depending on whether the order of thedataset is preserved or the data is randomized. You can specify the percentagesplit (the default is two-thirds training set and one-third test set).Experimental setups can be saved and reopened. You can make notes aboutthe setup by pressing the Notes button, which brings up an editor window.Serious Weka users soon find the need to open up an experiment and rerun itwith some modifications—perhaps with a new dataset or a new learning
442 CHAPTER 12 | THE EXPERIMENTERalgorithm. It would be nice to avoid having to recalculate all the results that havealready been obtained! If the results have been placed in a database rather thanan ARFF or CSV file, this is exactly what happens. You can choose JDBC databasein the results destination selector and connect to any database that has aJDBC driver. You need to specify the database’s URL and enter a username andpassword. To make this work with your database you may need to modify theweka/experiment/DatabaseUtils.props file in the Weka distribution. If you alteran experiment that uses a database, Weka will reuse previously computed resultswhenever they are available. This greatly simplifies the kind of iterative experimentationthat typically characterizes data mining research.12.3 Advanced setupThe Experimenter has an advanced mode. Click near the top of the panel shownin Figure 12.1(a) to obtain the more formidable version of the panel shown inFigure 12.3. This enlarges the options available for controlling the experiment—including, for example, the ability to generate learning curves. However, theFigure 12.3 Setting up an experiment in advanced mode.
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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 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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Page 472 and 473: 12.1 GETTING STARTED 439Dataset,Run
Page 476 and 477: 12.4 THE ANALYZE PANEL 443advanced
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Page 494 and 495: chapter 14Embedded Machine Learning
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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
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REFERENCES 491Fix, E., and J. L. Ho
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REFERENCES 493Gennari, J. H., P. La
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Conference on Knowledge Discovery a
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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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