Rewrite Essay Example | Topics and Well Written Essays - 1250 words - 1

edict - Essay ExampleWEKA enables the one of two options such as pruned tree or non pruned tree as shown in the figure. Figure 1 Properties of the Decision tree in the WEKA (J48) In addition to higher up features, the WEKA in addition performs the test options for entropy use and data correctification. Usage of the rearing set Evaluation of the classifier is based on the prediction of the instances of a class, which is trained on. Supplied Test Evaluation of the classifier is also performed on the prediction of the instances of a class, which is loaded from the file. scupper Validation By entering the number of faithful into the text field of the Fold in the WEKA explorer the classifier is evaluated. Percentage Split Data parcel is predicted by the evaluation of a classifier that takes the data out for the testing. The percentage field determines the specification of data held. During the train, data is employ and provided the value of percentage field that makes the impo rtant part. Value of the reminder is reserved for the testing purposes. By the default, value of percentage split is stated as the 66%. Data about 34% is employ for testing and remaining 66% is trained. Figure 2 WEKA with testing options Decision tree functioning is determined by examining the continue validation and percentage split in the provided medical dataset. Usage of Cross Validation for generation of decision tree In order to control the parts such as trainings set size and confidence by the process of cross validation, the flexibility is found in the decision tree of J48. Confidence factor is used to minimise or reduce the error enumerate of the classification. It is said that confidence factor is used to settle the problem of tree pruning. In order to affiliate the instances in a more accurate way, the classifier is given an opportunity by increasing the confidence factor and removing the noise of the training. The value of the confidence factor is 95% used for the dataset and leads to an outstanding outcome of 89.2% for the correct and classified ad instances and only 10.7% is the classified in decently as shown in the following figure. Figure 3 Use of cross validation based on the option J-48 decision tree to generate the results by WEKA. In the above figure, the calculation of J48 decision tree has been shown which includes correct values in details. Confusion Matrix is the important raze in the given figure, which describes the ways in which a classifier makes an error in the prediction of a class type. According to Dunham (2003) the confusion matrix provides the correctness of the solution for the given classification problem. Another term used as an alternative to the confusion matrix is the contingency table. Two classes having a single dataset contain a column and two rows for the confusion matrix as shown in the figure 4. Predicted Actual Figure 4 Confusion Matrix Here FP represents the incorrectly classified number of negatives as p ositives and called as the commission errors. TP represents correctly classified number of positives. TN represents the correct classification of negative numbers, and FN shows the incorrect classification of positive numbers as negative. These are called as the omission errors. prophetic accuracy becomes the way for measuring the performance of a classifier. Predictive accuracy is known as the calculated success rate determined by the use of prognostic accuracy as the confusion mat