C5 Causal Decision Tree

Authors

  • S. Arul Selvi  Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
  • S. Sowmiya  Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
  • R. Sangeetha  Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India

Keywords:

Decision tree, Causal relationship, Potential outcome model, Partial association

Abstract

A choice tree is fabricated best down from a root hub and includes apportioning the information into subsets that contain examples with comparative esteems (homogenous). On the off chance that the example is totally homogeneous the entropy is zero and if the example is a similarly partitioned it has entropy of one. C5.0 calculation and blue line demonstrates proposed calculation. With the assistance of diagram we can see that exactness of enhanced C5.0 is high when the information estimate is less. In any case, precision of C5.0 calculation diminishes with the expansion of information measure. Exactness of proposed show is superior to anything C5.0 for expansive information estimate.

References

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  2. P. R. Rosenbaum, Design of Observational Studies, ser. Springer Series in Statistics. Springer, 2010.
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  4. P. Spirtes, "Introduction to causal inference," Journal of Machine Learning Research, vol. 11, pp. 1643-1662, 2010.
  5. J. Pearl, Causality: Models, Reasoning, and Inference, 2nd ed. Cambridge University Press, 2009.

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Published

2018-04-30

Issue

Section

Research Articles

How to Cite

[1]
S. Arul Selvi, S. Sowmiya, R. Sangeetha, " C5 Causal Decision Tree, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.876-879, March-April-2018.