Data Mining: Practical Machine Learning Tools and ... - LIDeCC

516 INDEXlistOptions(), 482literary mystery, 358LMT, 408load forecasting, 24–25loan application, 22–23local discretization, 297locally weighted linear regression, 244,251–253, 253–254, 323locally weighted Naïve Bayes, 252–253Log button, 380logic programs, 75logistic model trees, 331logistic regression, 121–125LogitBoost, 328, 330, 331LogitBoost, 416logit transformation, 121log-likelihood, 122–123, 276, 277log-normal distribution, 268log-odds distribution, 268LWL, 414MM5¢ program, 384M5P, 408M5Rules, 409machine learning, 6main(), 453majority voting, 343MakeDensityBasedClusterer, 419MakeIndicator, 396, 398makeTree(), 472, 480Manhattan metric, 129manufacturing processes, 28margin, 324margin curve, 324market basket analysis, 27market basket data, 55marketing and sales, 26–28Markov blanket, 278–279Markov network, 283massive datasets, 346–349maximization, 265, 267maximum margin hyperplane, 215–217maxIndex(), 472MDL metric, 277MDL principle, 179–184mean absolute error, 177–179mean-squared error, 177, 178measurement errors, 59membership function, 121memorization, 76MergeTwoValues, 398merging, 257MetaCost, 319, 320MetaCost, 417metadata, 51, 349, 350metadata extraction, 353metalearner, 332metalearning algorithms in Weka, 414–418metric tree, 136minimum description length (MDL) principle,179–184miscellaneous classifiers in Weka, 405, 414missing values, 58classification rules, 201–202decision tree, 63, 191–192instance-based learning, 1291R, 86mixture model, 267–268model tree, 246–247statistical modeling, 92–94mixed-attribute problem, 11mixture model, 262–264, 266–268MLnet, 38ModelPerformanceChart, 431model tree, 76, 77, 243–251building the tree, 245missing values, 246–247nominal attributes, 246pruning, 245–246pseudocode, 247–250regression tree induction, compared, 243replicated subtree problem, 250rules, 250–251smoothing, 244, 251splitting, 245, 247what is it, 250momentum, 233monitoring, continuous, 28–29MultiBoostAB, 416