Regression Analyses or Decision Trees?

Year 2020, Volume: 18 Issue: 4, 251 - 260, 28.12.2020

Burcu Kocarık Gacar , İpek Deveci Kocakoç

https://doi.org/10.18026/cbayarsos.796172

Cited By: 3

Abstract

Decision tree algorithm is an important classification method in data mining techniques. A decision tree creates classification and regression models like a tree that has a root node, branches, and leaf nodes. Logistic regression which is an alternative method to regression analysis when the dependent variable is a dichotomy, is another technique used for classification purposes. Within the scope of this research, logistic regression, linear regression, classification tree, and regression tree were applied on the same data set. This study explores the most important variables determining the house price by using these four methods. Models’ performances and predictive powers were compared and the best model is determined. This comparison was performed using 414 real estate data on 5 independent variables and the dependent variable is house price. The findings showed that the classification tree model for real estate valuation data performs better than standard approaches.

Keywords

Regression, Logistic Regression, Classification and Regression Trees, Decision Trees

Regresyon Analizleri mi Karar Ağaçları mı?

Abstract

Karar ağaçları algoritması, veri madenciliği teknikleri içinde önemli bir sınıflandırma yöntemidir. Karar ağacı, kök düğümü, dalları ve yaprak düğümleri olan ağaç yapısında sınıflandırma ve regresyon modelleri oluşturur. Bağımlı değişken iki kategorili olduğunda regresyon analizine alternatif bir yöntem olarak tercih edilen lojistik regresyon analizi, sınıflandırma amacıyla kullanılan bir diğer tekniktir. Bu araştırma kapsamında aynı veri seti üzerinde lojistik regresyon, doğrusal regresyon, sınıflandırma ağacı ve regresyon ağacı yöntemleri uygulanmıştır. Bu dört yöntem kullanılarak konut fiyatını belirleyen en önemli değişkenler belirlenmiştir. Modellerin performansları ve tahmin güçleri karşılaştırılmış; en iyi sınıflandırma yapan model belirlenmeye çalışılmıştır. Bu karşılaştırma, 5 bağımsız değişken ve bağımlı değişken ev fiyatı olmak üzere, 414 gayrimenkul verisi kullanılarak yapılmıştır. Analiz sonucunda elde edilen bulgular, gayrimenkul değerleme verisi için sınıflandırma ağacı modelinin standart yaklaşımlardan daha iyi performans sergilediğini göstermiştir.

Keywords

Regresyon, Lojistik Regresyon, Sınıflandırma ve Regresyon Ağaçları, Karar Ağaçları

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