Comparative Performance Analysis of Random Forest and Logistic Regression Algorithms

被引：0
作者：
Malkocoglu, Ayse Berika Varol [1 ]
Malkocoglu, Sevki Utku [2 ]
机构：
[1] Maltepe Univ, Bilgisayar Muhendisligi Bolumu, Istanbul, Turkey
[2] Gebze Tekn Univ, Bilgisayar Muhendisligi Bolumu, Istanbul, Turkey
来源：
2020 5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK) | 2020年
关键词：
Random Forest; Logistic Regression; WEKA; MATLAB; Bank Marketing Data;
D O I：
10.1109/ubmk50275.2020.9219478
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Today, banks are trying to meet the needs of their existing customers with the marketing activities they do in digital media. It is known to produce statistical results in order to be able to predict the behavior of customers in artificial intelligence applications by storing large-scale data obtained through marketing studies. In this study, performance comparison between random forest and logistic regression algorithms was made by using real banking marketing data that includes the characteristics of customers. In addition, these algorithms were run on WEKA, Google Colab and MATLAB platforms to compare performance on different platforms. At the end of the study, the most successful result obtained with 94.8% accuracy, 93.9% sensitivity, 94.8% recall, 94.4% fl-score and 98.7% AUC value was achieved by random forest algorithm on WEKA platform. In addition, it has been shown that the obtained performance values produce better results compared to similar studies.
引用
收藏
页码：25 / 30
页数：6
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