A Comparison of Decision Tree and Logistic Regression Model
نویسنده
چکیده
ABSTRACT This paper applies a decision tree model and logistic regression models to a real transportation problem, compares results of these two methods and presents model building procedures as well. The data set is partitioned into train, validation and test data. Due to the skewness of some variables, the variable transformation technique has been conducted and a transformed logistic regression is built. The logistic regression models perform better than tree model, while the non-transformed logistic regression model and transformed regression model are indistinguishable. The non-transformed regression model is recommended for this transportation problem.
منابع مشابه
Comparison of Gestational Diabetes Prediction Between Logistic Regression, Discriminant Analysis, Decision Tree and Artificial Neural Network Models
Background and Objectives: Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder in pregnancy. In case of early detection, some of its complications can be prevented. The aim of this study was to investigate early prediction of GDM by logistic regression (LR), discriminant analysis (DA), decision tree (DT) and perceptron artificial neural network (ANN) and to compare these m...
متن کاملRanking stocks of listed companies on Tehran stock exchange using a hybrid model of decision tree and logistic regression
Much research has introduced linear or nonlinear models using statistical models and machine learning tools in artificial intelligence to estimate Iran's rate of return. The primary purpose of these methods is simultaneously use different independent variables to improve stock return rates' modeling. However, in predicting the rate of return, in addition to the modeling method, the degree of co...
متن کاملمقایسه مدل درخت تصمیم و رگرسیون لوجستیک در ارزیابی پوکی استخوان
Introduction: Early detection of osteoporosis is a key to preventing of it; but recognition, without the use of appropriate diagnostic methods, due to the complexity of risk factors and gradual bone loss process, is problem. The purpose of this study is to develop and efficiency evaluation a predictive model of osteoporosis using decision tree technique as a diagnostic method based on available...
متن کاملA Comparison of Decision Tree with Logistic Regression Model for Prediction of Worst Non-Financial Payment Status in Commercial Credit
Credit risk prediction is an important problem in the financial services domain. While machine learning techniques such as Support Vector Machines and Neural Networks have been used for improved predictive modeling, the outcomes of such models are not readily explainable and, therefore, difficult to apply within financial regulations. In contrast, Decision Trees are easy to explain, and provide...
متن کاملThe Comparison of Credit Risk between Artificial Neural Network and Logistic Regression Models in Tose-Taavon Bank in Guilan
One of the most important issues always facing banks and financial institutes is the issue of credit risk or the possibility of failure in the fulfillment of obligations by applicants who are receiving credit facilities. The considerable number of banks’ delayed loan payments all around the world shows the importance of this issue and the necessary consideration of this topic. Accordingly...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009