ttps://archive.ics.uci.edu/ml/datasets/glass+identification his dataset is available in the WEKA system folder. The dataset contains 214 instances nd 10 features (all continuous) and 1 class feature (type of class). This problem nvolves exploring/comparing two rule-based systems (1R and JRIP): 1. Divide the dataset into training (90%) and testing (10%) sets. Explain the strategy used to divide the datasets. 2. Generate rules with the 1R algorithm using the training set. Based on the Confusion Matrix, show the calculation for TP rate value, FP rate, Precision, Recall and F-measure for 3 of the seven classes. 3. Run the 1R algorithm using the test set. Based on the Confusion Matrix, show the calculation for TP rate value, FP rate, Precision, Recall and F-measure for SAME 3 (used in Step 2) of the seven classes. 4. Repeat steps 2 and 3 with the JRIP algorithm. 5. Compare the classification accuracy of 1R and JRIP. Comment on the number of rules. 6. Run the 1R and JRIP algorithms using 10 -fold cross validation. Comment on the classification accuracy results. 7. Why is the 10-fold classification accuracy for 1R and JRIP different from the ones in step 5 (with 90% training and 10% testing)