İnternetten Alişveri̇ş Yapan Haneleri̇n Rastgele Orman Yöntemi̇yle Tahmi̇n Edi̇lmesi̇

Abstract

The aim of the study is to determine the households shopping online in Turkey. During the modeling phase, the Random Forest method, which is frequently preferred in classification problems, was used. The data set in the TÜİK 2019 Household Budget Survey and gathered from 11521 households was used. The data set of the study was balanced with SMOTE and Random Undersampling methods. The cross-validation method was used to increase the accuracy of the study. The performances of the established models were compared and interpreted, and it was shown that the classifier performance could be increased with the correct use of sampling methods and cross-validation. In the training dataset, the model established by applying the SMOTE method was found to be more successful than the results of all criteria (F, DP, G-Means and MCC ) compared to other models. In the test data set, while it was observed that the model with the SMOTE method was more successful than the results of the F and MCC criteria, the model established with the Undersampling method was more successful according to the result of the G-Means criterion, and the model created without using any method was found to be successful according to the result of the DP criterion.

Estimation of Compressive Resistance of Briquettes Obtained from Groundnut Shells with Different Machine Learning Algorithms

“…This process is repeated "k" times. The average of the results determines the accuracy of the method [41]. The K-Fold cross-validation diagram is shown in Figure 6.…”

Assessing Fire Risk in Wildland–Urban Interface Regions Using a Machine Learning Method and GIS data: The Example of Istanbul’s European Side

“…This process was repeated "k" times. The average of the results determined the accuracy of the method [50]. The diagram of the k-fold cross-validation is shown in Figure 17.…”

“…Classification through machine learning methods is becoming a commonly used approach in various fields. Many studies in the literature indicate that machine learning methods are efficient in classification problems, offering high accuracy and minimal deviation [46][47][48][49][50][51].…”

Havayolu Taşımacılığında Müşteri Memnuniyetinin Topluluk Öğrenmesi Yöntemleri ile Belirlenmesi

“…Makine öğrenmesi, örnek verileri veya geçmiş deneyimleri kullanarak sürekli bir çıktının değerinin ya da kategorik bir çıktının sınıfının tahmin edilmesi süreci olarak tanımlanabilir (Alpaydın, 2010). Makine öğrenmesi yöntemleri sağlık, eğitim, mühendislik, sosyal ve ziraat gibi birçok alandaki regresyon ve sınıflandırma problemlerinde sıklıkla kullanılmaktadır (Ecer vd., 2018;Mariescu-Istodor ve Jormanainen, 2019;Kumar vd., 2020;Rajula vd., 2020;Duman vd., 2022;Ercan, 2021). Gerçekleştirilen çalışmada güncel makine öğrenmesi yöntemleri sivil hava ulaştırma alanına uygulanmıştır.…”

“…Rastgele Orman, Gradient Boosting, XGBoost ve LightGBM makine öğrenmesi yöntemleri, Topluluk Öğrenme yöntemleridir. Bu yöntemler özellikle sınıflandırma problemlerinde etkin ve başarılı bir şekilde kullanılmaktadır (Ercan, 2021;Üstüner vd., 2021;Noviantoro ve Huang, 2022). Popüler topluluk öğrenme yöntemlerinden Graident Boosting (GB), XGBoost ve Rastgele Orman (RO) ile modellemeler yapılmış ve sonuçları karşılaştırmalı olarak incelenmiştir.…”