Data Mining: Practical Machine Learning Tools and Techniques, Second Edition

522 INDEX

support vector, 216

support vector machine, 39, 188, 214, 340

support vector machine (SVM) classifier, 341

support vector machines with Gaussian

kernels, 234

support vector regression, 219–222

surrogate splitting, 247

SVMAttributeEval, 423

SVM classifier (Support Vector Machine), 341

SwapValues, 398

SymmetricalUncertAttributeEval, 423

symmetric uncertainty, 291

systematic data errors, 59–60

T

tabular input format, 119

TAN (Tree Augmented Naïve Bayes), 279

television preferences/channels, 28–29

tenfold cross-validation, 150, 151

Tertius, 420

test set, 145

TestSetMaker, 431

text mining, 351–356

text summarization, 352

text to attribute vectors, 309–311

Te x t Vi e w e r, 430

TF ¥IDF, 311

theory, 180

threat detection systems, 357

3-point average recall, 172

threefold cross-validation, 150

ThresholdSelector, 418

time series, 311

TimeSeriesDelta, 400

TimeSeriesTranslate, 396, 399–400

timestamp, 311

TN (True Negatives), 162

tokenization, 310

tokenization in Weka, 399

top-down induction of decision trees, 105

toSource(), 453

toString(), 453, 481, 483

toy problems.Seeexample problems

TP (True Positives), 162

training and testing, 144–146

training set, 296

TrainingSetMaker, 431

TrainTestSplitMaker, 431

transformations.Seeattribute transformations

transforming a multiclass problem into a two-

class one, 334–335

tree

AD (All Dimensions), 280–283

alternating decision, 329, 330, 343

ball, 133–135

decision.Seedecision tree

logistic model, 331

metric, 136

model, 76, 243.See alsomodel tree

numeric prediction, 76

option, 328–331

regression, 76, 243

Tree Augmented Naïve Bayes (TAN), 279

tree classifier in Weka, 404, 406–408

tree diagrams, 82

Trees(subpackages), 451, 453

Tree Visualizer, 389, 390

true negative (TN), 162

true positive (TP), 162

true positive rate, 162–163

True positive rate, 378

t-statistic, 156

t-test, 154

TV preferences/channels, 28–29

two-class mixture model, 264

two-class problem, 73

two-tailed test, 156

two-way split, 63

typographic errors, 59

U

ubiquitous data mining, 358–361

unacceptable contracts, 17

Unclassified instances, 377

Undo, 383

unit, 224

univariate decision tree, 199

universal language, 32

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