Predicting CPU Performance Score with Regression Analysis

Yıl 2025, Cilt: 16 Sayı: 1, 1 - 11

Güney Kaya , Emre Şen , Osman Altay

https://doi.org/10.24012/dumf.1493049

Öz

The purpose of this research is to use regression analysis to predict a CPU's performance score based on its features. CPU performance is incredibly important to evaluate when choosing a computer, along with system configuration and design. Support Vector Regression (SVR), Random Forest Regression (RFR), Multiple Linear Regression (MLR), Gradient Boosting Regression (GBR) and Neural Network Regression (NNR) are used to estimate the CPU's performance score. To test the algorithms, 30 percent of the data set was selected as test data and 70 percent as training data, separated randomly. As a result, the NNR has the highest of the coefficient of determination score which is 0.976, followed by GBR, 0.958. MLR, RFR and SVR algorithms have the R-squared score of 0.952, 0.934 and 0.865, respectively.

Anahtar Kelimeler

Regression Analysis, Machine Learning, Data Mining, CPU, CPU Performance

Regresyon Analizi ile MİB Performans Puanını Tahmin Etme

Öz

Bu araştırmanın amacı, bir MİB'in performans puanını özelliklerine göre tahmin etmek için regresyon analizini kullanmaktır. Bir bilgisayar seçerken MİB performansının yanı sıra sistem yapılandırması ve tasarımının değerlendirilmesi son derece önemlidir. Destek Vektör Regresyonu (DVR), Rastgele Orman Regresyonu (ROR), Çoklu Doğrusal Regresyon (ÇDR), Gradyan Arttırma Regresyonu (GAR) ve Yapay Sinir Ağları Regresyonu (YSA), MİB'in performans puanını tahmin etmek için kullanılmıştır. Algoritmaları test etmek için veri setinin yüzde 30'u test verileri, yüzde 70'i ise eğitim verileri olarak rastgele ayrıldı. Sonuç olarak, belirleme katsayısı en yüksek olan 0,976 ile YSA'dır ve onu 0,958 ile GAR takip etmektedir. ÇDR, ROR ve DVR algoritmaları sırasıyla 0,952, 0,934 ve 0,865 R-kare puanına sahiptir.

Anahtar Kelimeler

Regresyon Analizi, Makine Öğrenimi, Veri Madenciliği, MİB, MİB Performansı

Kaynakça

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