1. ***Classification Model*** Hosam Al-Samarraie, PhD. CITM-USM

2. Download Weka Click download And run it and follow The steps for installation. Just next, next, etc….

3. Pre-processing the data Data can be imported from a file in various formats: ARFF, CSV, binary Data can also be read from a URL or from SQL databases using JDBC Pre-processing tools in WEKA are called “ filters ” WEKA contains filters for: – Discretization, normalization, resampling, attribute selection, attribute combination, … 3

4. Building classification models “ Classifiers ” in WEKA are models for predicting nominal or numeric quantities Implemented schemes include: – Decision trees and lists, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, Bayes ’ nets, … “ Meta ” -classifiers include: – Bagging, boosting, stacking, error-correcting output codes, data cleansing, … 4

5. Start window Use to open the csv format and to convert to arff Use to open deploy Classification and prediction Use to compare different Classifiers

6. Explore window Insert the file 3. Similar to 2 but with more details about the performance 2. Use to select The attribute that Predict classes.

7. Select file Open the file Select the file File attributes Visual representation 1 2 3 4

8. Classify.. 1. After loading the file I select classify 2. in this window, I click on “Choose” To select the classifier for my data. 3. The common used one is J48 classifier, select and it will close automatically. 1 2 3

9. Start Make sure cross Validation is ticked Click start To run the Classification 1 2

10. Result This is the percentage of the correct classification. >50 consider ok. This is the roc for Measuring the performance >60 consider ok.. This is the precision Of the classification For each class.. Usually similar to roc. This is the confusion Matrix just another indictor For the performance of Classification. 1 2 3 4

11. Right click on The result file. Select visualize Tree… 5 6

12. Oval refers To the predictors Meaning that we have 3 predictors. 7

13. Attribute Selection Now we investigate which subset of attributes produces the best cross-validated classification accuracy for the algorithm we used on the dataset.

14. Identify the search method 1 2

15. Start 1 2

16. Interpretation of the output M, SD, SE of the selected attributes Percentage of correctly classified instance Precision Recall ROC Confusion matrix

17. In case… You still have the time to decide which classifier to use (algorithm). Since there are differences in the classifier’s accuracies, then it is recommended to compare between different classifiers.

18. Compare classifiers 1

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20. 4 5

21. 1 2 3 4 V indicate significant relation

22. Thank you.