Evaluating the Performance of Supervised Classification Models: Decision Tree and Naïve Bayes Using KNIME - Muzamil Basha, Syed; Singh Rajput, Dharmendra; Kumar Poluru, Ravi; Bharath Bhushan, S.; Abdul Khalandar Basha, Shaik (International Journal of Engineering and Technology, 2018)
The classification task is to predict the value of the target variable from the values of the input variables. If a target is provided as part of the dataset, then classification is a supervised task. It is important to analysis the performance of supervised classification models before using them in classification task. In our research we would like to propose a novel way to evaluated the performance of supervised     classification models like Decision Tree and Naïve Bayes using KNIME Analytics platform. Experiments are conducted on Multi variant dataset consisting 58000 instances, 9 columns associated specially for classification, collected from UCI Machine learning repositories  (http://archive.ics.uci.edu/ml/datasets/statlog+(shuttle)) and compared the performance of both the models in terms of Classification  Accuracy (CA) and Error Rate. Finally, validated both the models using Metric precision, recall and F-measure. In our finding, we found that  Decision tree acquires CA (99.465%) where as Naïve Bayes attain CA (90.358%). The F-measure of Decision tree is 0.984, whereas Naïve Bayes acquire 0.7045.