Decision Tree implementation in weka tool

Procedure:

Step1. We begin the experiment by loading the data (iris.arff) into weka.

Step2: next we select the “classify” tab and click “choose” button to select the “j48”classifier.

Step3: now we specify the various parameters. These can be specified by clicking in the text box to the right of the chose button. In this example, we accept the default values his default version does perform some pruning but does not perform error pruning.

Step4: under the “text “options in the main panel. We select the 10-fold cross validation as our evaluation approach. Since we don’t have separate evaluation data set, this is necessary to get a reasonable idea of accuracy of generated model.

Step-5: we now click”start”to generate the model .the ASCII version of the tree as well as evaluation statistic will appear in the right panel when the model construction is complete.

Step-6: note that the classification accuracy of model is about 69%.this indicates that we may find more work. (Either in preprocessing or in selecting current parameters for the classification)

Step-7: now weka also lets us a view a graphical version of the classification tree. This can be done by right clicking the last result set and selecting “visualize tree” from the pop-up menu.

Step-8: we will use our model to classify the new instances.

Step-9: In the main panel under “text “options click the “supplied test set” radio button and then click the “set” button. This will show pop-up window which will allow you to open the file containing test instances.

=== Run information ===

Scheme:       weka.classifiers.trees.J48 -C 0.25 -M 2

Relation:     iris

Instances:    150

Attributes:   5

              sepallength

              sepalwidth

              petallength

              petalwidth

              class

Test mode:    10-fold cross-validation

=== Classifier model (full training set) ===

J48 pruned tree

------------------

petalwidth <= 0.6: Iris-setosa (50.0)

petalwidth > 0.6

|   petalwidth <= 1.7

|   |   petallength <= 4.9: Iris-versicolor (48.0/1.0)

|   |   petallength > 4.9

|   |   |   petalwidth <= 1.5: Iris-virginica (3.0)

|   |   |   petalwidth > 1.5: Iris-versicolor (3.0/1.0)

|   petalwidth > 1.7: Iris-virginica (46.0/1.0)

Number of Leaves  :             5

Size of the tree :     9

Time taken to build model: 0 seconds

=== Stratified cross-validation ===

=== Summary ===

Correctly Classified Instances         144               96      %

Incorrectly Classified Instances         6                4      %

Kappa statistic                          0.94 

Mean absolute error                      0.035

Root mean squared error                  0.1586

Relative absolute error                  7.8705 %

Root relative squared error             33.6353 %

Coverage of cases (0.95 level)          96.6667 %

Mean rel. region size (0.95 level)      33.7778 %

Total Number of Instances              150    

=== Detailed Accuracy By Class ===

                 TP Rate  FP Rate  Precision  Recall   F-Measure  MCC      ROC Area  PRC Area  Class

                 0.980    0.000    1.000      0.980    0.990      0.985    0.990     0.987     Iris-setosa

                 0.940    0.030    0.940      0.940    0.940      0.910    0.952     0.880     Iris-versicolor

                 0.960    0.030    0.941      0.960    0.950      0.925    0.961     0.905     Iris-virginica

Weighted Avg.    0.960    0.020    0.960      0.960    0.960      0.940    0.968     0.924    

=== Confusion Matrix ===

  a  b  c   <-- classified as

 49  1  0 |  a = Iris-setosa

  0 47  3 |  b = Iris-versicolor

  0  2 48 |  c = Iris-virginica