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

10.2 EXPLORING THE EXPLORER 387+ 0.012 * MYCT+ 0.0162 * MMIN+ 0.0086 * MMAX+ 0.8332 * CACH- 1.2665 * CHMIN+ 1.2741 * CHMAX- 107.243Number of Rules : 2Time taken to build model: 1.37 seconds=== Cross-validation ====== Summary ===Correlation coefficient 0.9766Mean absolute error 13.6917Root mean squared error 35.3003Relative absolute error 15.6194 %Root relative squared error 22.8092 %Total Number of Instances 209Figure 10.11 (continued)make sense for numeric prediction). Section 5.8 (Table 5.8) explains themeaning of the various measures.Ordinary linear regression (Section 4.6), another scheme for numeric prediction,is found under LinearRegression in the functions section of the menu inFigure 10.4(a). It builds a single linear regression model rather than the two inFigure 10.11; not surprisingly, its performance is slightly worse.To get a feel for their relative performance, let’s visualize the errors theseschemes make, as we did for the Iris dataset in Figure 10.6(b). Right-click theentry in the history list and select Visualize classifier errors to bring up the twodimensionalplot of the data in Figure 10.12. The points are color coded byclass—but in this case the color varies continuously because the class is numeric.In Figure 10.12 the Vendor attribute has been selected for the X-axis and theinstance number has been chosen for the Y-axis because this gives a good spreadof points. Each data point is marked by a cross whose size indicates the absolutevalue of the error for that instance. The smaller crosses in Figure 10.12(a) (forM5¢), when compared with those in Figure 10.12(b) (for linear regression),show that M5¢ is superior.