Decision Trees Using Weka and Rattle
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1 9/28/2017 MIST.6060 Business Intelligence and Data Mining 1 Data Mining Software Decision Trees Using Weka and Rattle We will mainly use Weka (( an open source datamining software suite written in Java. We will also use Rattle ( another open source data-mining software suite written in R ( Input Data Format in Weka ARFF file: weather.arff (which contains the dataset shown on page 6 of the Decision Trees lecture notes). weather.missing1.arff (which contains the dataset shown on page 8 of the Decision Trees lecture notes). CSV file: weather.csv (which contains the dataset shown on page 6 of the Decision Trees lecture notes). Decision Trees Using Weka Following the steps below, run the decision tree algorithms in Weka. 1. Click Explorer. Click Open file, find and open the weather.arff (or weather.csv) file. 2. Click Classify / Choose / trees / J48 (which is C4.5). 3. In the Test options area, select Use training set, and then click Start. Since using the training set as the test set will produce the same result as that without using a test set, the output results are essentially the results on the training set, which have zero training error.
2 9/28/2017 MIST.6060 Business Intelligence and Data Mining 2
3 9/28/2017 MIST.6060 Business Intelligence and Data Mining 3 4. The text representation of the tree above is not very easy to read. Weka allows you to view the tree in a graphical format. Note that each time the Start button is clicked, a new classifier is trained and tested, and a new entry is written into the Result list panel in the lower left corner. To get the graphical tree, right click the corresponding entry and then select Visualize tree. A new screen that shows the graphical tree will appear. From this tree, it can be easily seen that the number of leaves (rectangles) is 5 and the size of the tree (the number of total nodes) is 8, as reported in the Weka output. Note that in the output, the number on the right side of a leaf (within the parentheses) indicates the number of records that are routed to the leaf (subset). For example, there are two records having {outlook = sunny AND humidity 75}, which have play = yes.
4 9/28/2017 MIST.6060 Business Intelligence and Data Mining 4 5. Now, in the Test options area, select Cross-validation with 10 folds, and then click Start. This will produce the following output.
5 9/28/2017 MIST.6060 Business Intelligence and Data Mining 5 The error rate now is much higher than the training error rate, but it is a better estimate for the error rate when the tree is used for classifying future data, because it is obtained using test data that is not used in building (including growing and pruning) the tree. Note that C4.5 s default pruning method is pessimistic-error pruning, which does not require using validation data for pruning. So, the tree here is the pruned tree (which happens to be the same as the unpruned tree shown on step 3 above). 6. Next, click the Preprocess tab on the upper left corner. Click Open file, find and open the weather.missing1.arff file. This leads to the following screen. Note that there is one missing value in the outlook attribute, as indicated in the Selected attribute section.
6 9/28/2017 MIST.6060 Business Intelligence and Data Mining 6 7. Click Classify / Choose / trees / J48. With default 10-fold cross-validation, click Start. This yields the following output. Comparing to the results in step 4, the test error rate is increased with a missing value. The numbers within the parentheses indicate the numbers of records that are routed to the leaf (subset), as well as the numbers of misclassified training records. For example, (3.38/0.38) for the leaf (subset) having {outlook = sunny AND humidity > 75} indicates that there are 3.38 records in this leaf and 0.38 of them are misclassified. The fraction numbers are caused by the weighting method for handling missing values. Therefore, it is better to think about them as weights rather than counts.
7 9/28/2017 MIST.6060 Business Intelligence and Data Mining 7 Decision Trees Using Rattle 1. Click Data. In the Filename box, find and open the weather.arff (or weather.csv) file (Click the File radio button for a CSV file or the ARFF radio button for an ARFF file). Deselect Partition. Click Execute. 2. Click Model. In the Min Bucket box, specify 2. Click Execute.
8 9/28/2017 MIST.6060 Business Intelligence and Data Mining 8 3. Click Draw. 4. Click Evaluate and then Execute.
9 9/28/2017 MIST.6060 Business Intelligence and Data Mining 9 5. Click Data again. Select Partition and use the default partition values. Click Execute. Then, click Model. In the Min Bucket box, specify 2. Click Execute.
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