516 INDEX
listOptions(), 482
literary mystery, 358
LMT, 408
load forecasting, 24–25
loan application, 22–23
local discretization, 297
locally weighted linear regression, 244,
251–253, 253–254, 323
locally weighted Naïve Bayes, 252–253
Logbutton, 380
logic programs, 75
logistic model trees, 331
logistic regression, 121–125
LogitBoost, 328, 330, 331
LogitBoost, 416
logit transformation, 121
log-likelihood, 122–123, 276, 277
log-normal distribution, 268
log-odds distribution, 268
LW L, 414M
M5¢program, 384
M5P, 408
M5Rules, 409
machine learning, 6
main(), 453
majority voting, 343
MakeDensityBasedClusterer, 419
MakeIndicator, 396, 398
makeTree(), 472, 480
Manhattan metric, 129
manufacturing processes, 28
margin, 324
margin curve, 324
market basket analysis, 27
market basket data, 55
marketing and sales, 26–28
Markov blanket, 278–279
Markov network, 283
massive datasets, 346–349
maximization, 265, 267
maximum margin hyperplane, 215–217
maxIndex(), 472
MDL metric, 277MDL principle, 179–184
mean absolute error, 177–179
mean-squared error, 177, 178
measurement errors, 59
membership function, 121
memorization, 76
MergeTwoValues, 398
merging, 257
MetaCost, 319, 320
MetaCost, 417
metadata, 51, 349, 350
metadata extraction, 353
metalearner, 332
metalearning algorithms in Weka, 414–418
metric tree, 136
minimum description length (MDL) principle,
179–184
miscellaneous classifiers in Weka, 405, 414
missing values, 58
classification rules, 201–202
decision tree, 63, 191–192
instance-based learning, 129
1R, 86
mixture model, 267–268
model tree, 246–247
statistical modeling, 92–94
mixed-attribute problem, 11
mixture model, 262–264, 266–268
MLnet, 38
ModelPerformanceChart, 431
model tree, 76, 77, 243–251
building the tree, 245
missing values, 246–247
nominal attributes, 246
pruning, 245–246
pseudocode, 247–250
regression tree induction, compared, 243
replicated subtree problem, 250
rules, 250–251
smoothing, 244, 251
splitting, 245, 247
what is it, 250
momentum, 233
monitoring, continuous, 28–29
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