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

INDEX 513

Fisher, R. A., 15

flat file, 45

F-measure, 172

FN (false negatives), 162

folds, 150

forward pruning, 34, 192

forward selection, 292, 294

forward stagewise additive modeling, 325–327

Fourier analysis, 25

FP (false positives), 162

freedom, degrees of 93, 155

functional dependencies, 350

functions in Weka, 404–405, 409–410

G

gain ratio, 104

GainRatioAttributeEval, 423

gambling, 160

garbage in, garbage out.Seecost of errors; data

cleaning; error rate

Gaussian-distribution assumption, 92

Gaussian kernel function, 252

generalization as search, 30–35

bias, 32–35

enumerating concept space, 31–32

generalized distance functions, 241–242

generalized exemplars, 236

general-to-specific search bias, 34

genetic algorithms, 38

genetic algorithm search procedures, 294,

341

GeneticSearch, 424

getOptions(), 482

getting to know your data, 60

global discretization, 297

globalInfo(), 472

global optimization, 205–207

Gosset, William, 184

gradient descent, 227, 229, 230

Grading, 417

graphical models, 283

GraphViewer, 431

gray bar in margin of textbook (optional

sections), 30

greedy search, 33

GreedyStepwise, 423–424

growing set, 202

H

Hamming distance, 335

hand-labeled data, 338

hapax legomena, 310

hard instances, 322

hash table, 280

hazard detection system, 23–24

hidden attributes, 272

hidden layer, 226, 231, 232

hidden units, 226, 231, 234

hierarchical clustering, 139

highly-branching attribute, 86

high-performance rule inducers, 188

histogram equalization, 298

historical literary mystery, 358

holdout method, 146, 149–150, 333

homeland defense, 357

HTML, 355

hypermetrope, 13

hyperpipes, 139

Hyperpipes, 414

hyperplane, 124, 125

hyperrectangle, 238–239

hyperspheres, 133

hypertext markup language (HTML), 355

hypothesis testing, 29

I

IB1, 413

IB3, 237

IBk, 413

ID3, 105

Id3, 404

identification code, 86, 102–104

implementation—real-world schemes,

187–283

Bayesian networks, 271–283

classification rules, 200–214

clustering, 254–271

decision tree, 189–199

instance-based, 236–243

linear models, 214–235

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