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

10.2 EXPLORING THE EXPLORER 391Figure 10.14 Configuring a metalearner for boosting decision stumps.Classify panel and choose the classifier AdaboostM1 from the meta section of thehierarchical menu. When you configure this classifier by clicking it, the objecteditor shown in Figure 10.14 appears. This has its own classifier field, which weset to DecisionStump (as shown). This method could itself be configured byclicking (except that DecisionStump happens to have no editable properties).Click OK to return to the main Classify panel and Start to try out boosting decisionstumps up to 10 times. It turns out that this mislabels only 7 of the 150instances in the Iris data—good performance considering the rudimentarynature of decision stumps and the rather small number of boosting iterations.Clustering and association rulesUse the Cluster and Associate panels to invoke clustering algorithms (Section6.6) and methods for finding association rules (Section 4.5). When clustering,Weka shows the number of clusters and how many instances each cluster contains.For some algorithms the number of clusters can be specified by setting aparameter in the object editor. For probabilistic clustering methods, Weka measuresthe log-likelihood of the clusters on the training data: the larger this quantity,the better the model fits the data. Increasing the number of clustersnormally increases the likelihood, but may overfit.The controls on the Cluster panel are similar to those for Classify. You canspecify some of the same evaluation methods—use training set, supplied testset, and percentage split (the last two are used with the log-likelihood). A further