6. 1R and ID3 have the same accuracy. Did they classify every instance the same way to achieve that accuracy? Justify your answer. Test options Classiftier output O Use training set O Supplied test set Stratified cross-validation -Summary- Set Cross-validation Folds 10 Percentage split %66 0.8333 29.1667 Correctly Classified Instances Incorreetly Cleanified instances Kappa statistic Hean abaolute erro Root mean squared error Relative absolute error Root relative squared error Total Nunber of Instances 17 0.5 More options. 0.1944 0.441 1.4706 100-965 % Nom) contact lenses 24 Start stop Detailed Accuracy By class Result list (right-click for options) 17 49.50o -ules.ZeroR 22:11:37-rules.OneR 22 14 44-trees ld3 22 16 52-trees J48 TR Rate FP Rate Precision Recall FHeasure MOC 1.000 0.368 0-417 0.000 0.000 0.8000,000.000 0.800 .8090.7750.900 0.925 on ROC Area PRC Area Cla .5000.167 hr 0.816 0.693 1.000 0.5880.513 0.816 0.417sof 0.000 Weighted Avg. o.708 0.077 0.708 Confusion Matrix a bcclaasitied as 5 o0-oft 4 OOIb hard 3 0 12 Ic none est Classifier output O Use training set O Supplied test set Set. Stratified cross-validation Sumary Cross-validaton Folds 10 Percentage split % 66 Correctly Classified Instances Incorrectly Classified Instances Kappa statistic Hean abaolute error Root mean squared error Relative absolute error Root relative squared error Total Number of Instances 17 70.8333 29.1667 0.4381 0.1944 0.441 51.4706 More options Nom) contactHenses 100.965 24 Start Stop Result list (right click for options) Detailed Accuracy By Class 7 49 50-rules ZeroR 221137-rulesOneR 22 id3 TP Rate FP Rate Precision Recall -Heanure MCCROC Area PRC Area CLa 0.747 0.874 0.682 sot 0.169 0.5750.208 har 0.800 0.053 0.8000.800 0.800 0.2500.100 0.333 .B00 0.4440.7500 0.250 0.286 14.44-trees 22 16 52-trees J48 0.800 0.774 0.3 0.678 0.725 non Weighted Avg. 0.708 0.305 0.691 .708 0.698 0.412 0.701 0.630 Confusion Matrix abc