Determining the Happiness Class of Countries with Tree-Based Algorithms in Machine Learning

Öz

Today, the concept of happiness is a frequently researched subject in the fields of economy, medicine, and social and political fields, aswell as psychology. It has been an important research area for everyone, from policymakers to companies, to determine the factors affecting happiness. With machine learning algorithms, it is possible to make classifications with very high accuracy. The aim of this study is to use tree-based machine learning algorithms to classify the happiness scores of countries. In order to accomplish this, data from the World Happiness Index published in 2022 were used. On these data, tree-based algorithms CART, tree-based ensemble algorithms Bagging, and Random Forest were used. The test data of the model were obtained with 85% precision, recall, and F1 metrics, which were calculated using Bagging and Random Forest algorithms. The outcomes of the models obtained during the study were interpreted.

Anahtar Kelimeler

• Machine learning
• World Happiness Index
• Ensemble learning

Makine Öğrenmesinde Ağaç Tabanlı Algoritmalarla Ülkelerin Mutluluk Sınıfının Belirlenmesi

Öz

Mutluluk kavramı günümüzde psikoloji alanı dışında ekonomi, tıp, sosyal ve politik alanlarda da sıklıkla araştırılan bir konu haline gelmiştir. Mutluluğu etkileyenfaktörlerin belirlenmesi, politika yapıcılardan işletmelere kadar önemli bir araştırma alanı olmuştur. Makine öğrenmesi algoritmaları ile yüksek doğrulukta sınıflandırmalar çalışmaları yapmak mümkündür. Bu çalışmada, ağaç tabanlı makine öğrenmesi algoritmaları kullanılarak ülkelerin mutluluk puanlarının sınıflandırılması amaçlanmaktadır. Bu amaçla 2022 yılında yayınlanan Dünya Mutluluk Endeksi’nden alınan veriler kullanılmıştır. Bu veriler üzerinde ağaç tabanlı algoritmalar SRT, ağaç tabanlı topluluk algoritmaları torbalama ve rastgele orman kullanılmıştır. Torbalama ve rastgele orman algoritmaları ile elde edilen modelin test verilerinde %85 kesinlik, duyarlılık ve F1 metrikleri hesaplanmıştır. Çalışmada elde edilen bu modellerin sonuçları yorumlanmıştır.

Anahtar Kelimeler

• Makine öğrenmesi
• Dünya Mutluluk Endeksi
• Topluluk öğrenmesi

Kaynakça

1. Bel, L., Allard, D., Laurent, J. M., Cheddadi, R. & Bar-Hen, A. (2009). CART algorithm for spatial data: Application to environmental and ecological data. Computational Statistics and Data Analysis. 53. 3082-3093. https://doi.org/10.1016/j.csda.2008.09.012 google scholar
2. Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324 google scholar
3. Carlsen, L. (2018). Happiness as a sustainability factor. The World Happiness Index: A posetic-based data analysis. Sustain Sci. 13. 549-571. https://doi.org/10.1007/s11625-017-0482-9 google scholar
4. Chaudhary, M, Dixit, S. & Sahni, N. (2020). Network learning approaches to study world happiness. A Preprint. https://arxiv.org/pdf/2007.09181.pdf google scholar
5. Dao, T. K. (2017). Government expenditure and happiness: Direct and indirect effects. Institute of Social Studies, Netherlands. Retrieved from https://thesis.eur.nl/pub/41656/Dao-Tung-K.-.pdf google scholar
6. Doğruel, M & Fırat, S. Ü. (2021). Veri madenciliği karar ağaçları kullanarak ülkelerin inovasyon değerlerinin tahmini ve doğrusal regresyon modeli ile karşılaştırmalı bir uygulama. Istanbul Business Research, 50(2), 465-493. https://www.doi.org/10.26650/ibr.2021.50.015019 google scholar
7. Efeoğlu, E. (2022). Kablosuz sinyal gücünü kullanarak iç mekan kullanıcı lokalizasyonu için karar ağaçları algoritmalarının karşılaştırılması. Acta Infologica. https://doi.org/10.26650/acin.1076352 google scholar
8. Erdem, Z. U., Uslu, B. Ç. & Fırat, S. Ü. (2021). Customer churn prediction analysis in a telecommunication company with machine learning algorithms. Journal of Industrial Engineering, 32(3). 496-512. google scholar

9. Farooq, S.A. & Shanmugam, S.K. (2022). A performance analysis of supervised machine learning techniques for COVID-19 and happiness report dataset. In: S. Shakya, V.E. Balas, S. Kamolphiwong, KL. Du (Eds) Sentimental analysis and deep learning. Singapore Springer. google scholar
10. Garces, E. J., Adriatico C. & Timbal, L. R. E. (2019). Analysis on the relationships on the global distribution of the World Happiness Index and selected economic development indicators. Open Access Library Journal, 6, 1-16. https://doi.org/10.4236/oalib.110545 google scholar
11. Grabczewski, K., & Duch,W. (1999, June). A general purpose separability criterion for classification systems. Proceedings of the 4th Conference on Neural Networks and Their Application. Zakopane, Poland, 203-208. google scholar
12. Grabczewski, K. (2011), Validated decision trees versus collective decisions. In P. Jedrzejowicz & N. T Nguyen (Eds.) Computational Collective Intelligence Technologies and Applications Third International Conference (pp.324-351). Verlag Berlin Heidelberg: Springer. google scholar
13. Gregorutti, B., Michel, B. & Saint-Pierre, P. (2017). Correlation and variable importance in Random Forests. Stat Comput, 27, 659–67. https://doi.org/10.1007/s11222-016-9646-1 google scholar
14. Helliwell, J. F., Layard, R., Sachs, J. D., De Neve, J.-E., Aknin, L. B., & Wang, S. (Eds.). (2022). World Happiness Report 2022. New York: Sustainable Development Solutions Network. google scholar
15. Ibnat, Gyalmo, Alom, Abdul Awal and Azim (2021, December). Understanding world happiness using machine learning techniques. International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2). Rajshahi, Bangladesh. https://www.doi.org/10.1109/IC4ME253898.2021.9768407 google scholar
16. James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. New York: Springer. google scholar
17. Jannani, A. Sael, N. & Benabbou, F. (2021, December). Predicting quality of life using machine learning: Case of World Happiness Index. 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Alkhobar, Saudi Arabia. https://www.doi.org/10.1109/ISAECT53699.2021.9668429 google scholar
18. Khder, M. A., Sayfi, M. A. & Fujo, S.W. (2022). Analysis ofWorld Happiness Report dataset using machine learning approaches. International Journal of Advances in Soft Computing and its Applications. 14(1). 14-34. https://doi.org/10.15849/IJASCA.220328.02 google scholar
19. Kıroğlu, B. S. & Yıldırım, K. (2022). Mutluluk ve belirleyicileri: Türkiye için bir analiz. Journal of Emerging Economies and Policy. 7(2). 5070. google scholar
20. Li, A. (2022). Stock forest model based on Random Forest. In X. Huang&F. Zhang (Eds.) Economic and business management. The Netherlands: CRP Press. google scholar
21. Okumuş, F., Ekmekçioğlu, A. & Kara, S. S. (2021). Modelling ships main and auxiliary engine powers with regression-based machine learning algorithms. Polish Maritime Research, 28(1). 83-96. https://www.doi.org/10.2478/pomr-2021-0008 google scholar
22. Onan, A. (2018). A Clustering Based Classifier Ensemble Approach to Corporate Bankruptcy Prediction. The Journal of Operations Research, Statistics, Econometrics and Management Information Systems, 6(2). 365-376. google scholar
23. Öztürk, S. & Suluk, S. (2020). Mutluluk Ekonomisi: G8 ülkeleri açısından ekonomik büyüme ve mutluluk arasındaki ilişkinin incelenmesi. Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. (37). 226-249. google scholar
24. Rokach, L. (2016). Decision forest: Twenty years of research. Information Fusion, 27. 111-125. http://dx.doi.org/10.1016/j.inffus.2015.06.005 google scholar
25. Suchetana, B., Rajagopalan, B. & Silverstein, J. (2017). Assessment of wastewater treatment facility compliance with decreasing ammonia discharge limits using a regression tree model. Science of the Total Environment, 598, 249- 257. https://doi.org/10.1016/j.scitotenv.2017.03.236 google scholar
26. Ulkhaq, M. M. & Adyatama, A. (2021). Clustering countries according to the world happiness report 2019. Engineering and Applied Science Research, 48(2). 137-150. google scholar
27. World Happiness Report, 2022a, “Data for table 2.1”, https://worldhappiness.report/ed/2022/#appendices-and-data , (29.07.2022) google scholar
28. World Happiness Report, 2022b, “FAQ”, https://worldhappiness.report/faq/ , (29.07.2022) google scholar