Evaluating the Performance of Supervised Classification Models: Decision Tree and Naïve Bayes Using KNIME

  • Authors

    • Syed Muzamil Basha
    • Dharmendra Singh Rajput
    • Ravi Kumar Poluru
    • S. Bharath Bhushan
    • Shaik Abdul Khalandar Basha
    2018-09-22
    https://doi.org/10.14419/ijet.v7i4.5.20079
  • Classification Accuracy, Decision Tree, Error Rate, F-measure, KNIME Analytics platform, Naïve Bayes, Precision, Recall.

  • 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.

     

     

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    Muzamil Basha, S., Singh Rajput, D., Kumar Poluru, R., Bharath Bhushan, S., & Abdul Khalandar Basha, S. (2018). Evaluating the Performance of Supervised Classification Models: Decision Tree and Naïve Bayes Using KNIME. International Journal of Engineering & Technology, 7(4.5), 248-253. https://doi.org/10.14419/ijet.v7i4.5.20079