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

(Section 6.1, page 190). It deals with missing values by splitting instances into pieces, as C4.5 does. You can set the minimum ...

10.4 LEARNING ALGORITHMS 409 value (or distribution) of the uncovered training instances. The information gain (nominal class) o ...

regression to subsamples of the data and outputs the solution that has the small- est median-squared error. SMOimplements the se ...

10.4 LEARNING ALGORITHMS 411 Neural networks MultilayerPerceptronis a neural network that trains using backpropagation (Section ...

attribute (colored green); a hidden layer next to it (red) to which all the input nodes are connected; and an output layer at th ...

10.4 LEARNING ALGORITHMS 413 there are predefined values that can be used instead of integers:iis the number of attributes,othe ...

LBR(for Lazy Bayesian Rules) is a Bayesian classifier that defers all process- ing to classification time. For each test instanc ...

learner. In the case of classification, predictions are generated by averaging prob- ability estimates, not by voting. One param ...

Boosting AdaBoostM1implements the algorithm described in Section 7.5 (page 321; Figure 7.7). It can be accelerated by specifying ...

Combining classifiers Vo t e provides a baseline method for combining classifiers by averaging their probability estimates (clas ...

The third metalearner,ThresholdSelector, optimizes the F-measure (Section 5.7) by selecting a probability threshold on the class ...

SimpleKMeansclusters data using k-means; the number of clusters is specified by a parameter.Cobwebimplements both the Cobweb alg ...

reached a lower bound of 10%, whichever occurs first. (These default values can be changed.) There are four alternative metrics ...

attributes that fall below a chosen cutoff point. This is achieved by selecting one of the eight single-attribute evaluators in ...

nation. The status line refers you to the error log for the message (see the end of Section 10.1). Attribute subset evaluators S ...

tribute the counts among other values in proportion to their frequency. ChiSquaredAttributeEvalevaluates attributes by computing ...

deleting the best remaining attribute decreases the evaluation metric. In an alternative mode, it ranks attributes by traversing ...

with one of the single-attribute evaluators in the lower part of Table 10.9—not an attribute subset evaluator.Rankernot only ran ...

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With the Knowledge Flow interface, users select Weka components from a tool bar, place them on a layout canvas, and connect them ...