COMPARATION LOGISTIC REGRESSION AND DECISION TREE METHOD TO DISTRIBUTION TYPE OF WORKS IN JAKARTA
Keywords:
Logistic Regression, Decision Tree, ClassificationAbstract
In the digital era, the data is one of the components that are important in decision making. Data must be processed first so that it can be understood by the recipient data. The results of data processing is called information. In this study, the method used are Logistic Regression and Decision Tree. Both of these methods are included in the classification method. The purpose of this study was to determine the accuracy of the data from implementation of methods logistic regression and decision tree. This research was conducted using the Python programming language and the Visual Studio code.
References
Hilbe, Joseph M. (2009). Logistic Regression Models. Chapman & Hall/CRC Press.
Nia Rahma Kurnianda & Yunita Sartika Sari. Analysis and Design of Information System for Self-Journal on Food Based Dietary Assessment Record for Diabetes Patients. International Research Journal of Computer Science (IRJCS). Volume 06 Issue 5. 2018
Ranggadara, Indra & Suhendra "Naive Bayes Algorithm with Chi Square and NGram Feature for Reviewing Laptop Product on Amazon Site.".IRJCS, Vol 7, issue 2.2018
William H. Kruskal and Judith M. Tanur, ed. (1982), "Linear Hypotheses," International Encyclopedia of Statistics. Free Press, v. 1,
Lindley, D.V. (1987). "Regression and correlation analysis," New Palgrave: A Dictionary of Economics, v. 4, pp. 120–23.
Birkes, David and Yadolah Dodge, Alternative Methods of Regression. ISBN 0-471-56881-3
Chatfield, C. (1993) "Calculating Interval Forecasts," Journal of Business and Economic Statistics, 11. pp. 121–135.
Corder, G.W. and Foreman, D.I. (2009).Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach Wiley, ISBN 978-0-470-45461-9
Draper, N.R. and Smith, H. (1998).Applied Regression Analysis Wiley Series in Probability and Statistics
Fox, J. (2017). Applied Regression Analysis, Linear Models and Related Methods. Sage
Hardle, W., Applied Nonparametric Regression (1990), ISBN 0-521-42950-1
Meade, N. and T. Islam (1995) "Prediction Intervals for Growth Curve Forecasts," Journal of Forecasting, 14, pp. 413–430.
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