Classification of Categorical Outcome Variable Based on Logistic Regression and Tree Algorithm
Pratibha V. Jadhav1, Vaishali V. Patil2, Sharad D. Gore3
1Mrs. Pratibha Vijay Jadhav, Pursuing Ph.D. in Statistics from J.J.T.University, Jhunjhunu, Rajasthan, India.
2Dr. Vaishali Vilas Patil, Assistant Professor in TC College Baramati Pune Maharashtra India.
3Dr. Sharad Damodar Gore, Professor in JJTU Rajasthan India.

Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4685-4690 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6844018520/2020©BEIESP | DOI: 10.35940/ijrte.E6844.018520

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Abstract: Logistic regression is most popular techniques incorporated in traditional statistics. Usually, this regression is applicable when the dependent variable is of categorical binary in nature. In the field of Statistics and Machine learning, classification of data is critical to discriminate to which set of clusters a new observation belongs, in the base of training set of a data containing observation whose group relationship is known. In this paper, we are focusing on the concepts of Logistic regression and classification tree. A large data taken from UCI (Machine learning Repository) incorporated for this research work. The aim of study is to distinguish the results obtained from Logistic regression and decision tree. At the end, decision tree gives better results than Logistic regression.
Keywords: Multiple Logistic Regression, CART, Misclassification Error.
Scope of the Article: Classification.