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Journals / Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi / 2026 / Cilt: 16 Sayı: 1

Predicting CPU Performance Score with Regression Analysis

Regresyon Analizi ile MİB Performans Puanını Tahmin Etme

Güney Kaya(CELAL BAYAR UNIVERSITY, HASAN FERDİ TURGUTLU FACULTY OF TECHNOLOGY, DEPARTMENT OF SOFTWARE ENGINEERING)0009-0007-7687-7350
Emre Şen(CELAL BAYAR UNIVERSITY, HASAN FERDİ TURGUTLU FACULTY OF TECHNOLOGY, DEPARTMENT OF SOFTWARE ENGINEERING)0009-0006-8640-8374
Osman Altay(CELAL BAYAR UNIVERSITY, HASAN FERDİ TURGUTLU FACULTY OF TECHNOLOGY, DEPARTMENT OF SOFTWARE ENGINEERING)0000-0003-3989-2432
Pages
1–11
Publication Date
2025-03-26
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

MLR, RFR and SVR algorithms have the R-squared score of 0.952, 0.934 and 0.865, respectively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The purpose of this research is to use regression analysis to predict a CPU's performance score based on its features.
📊
Key Findings & Data:CPU performance is incredibly important to evaluate when choosing a computer, along with system configuration and design.
💡
Conclusion & Impact:MLR, RFR and SVR algorithms have the R-squared score of 0.952, 0.934 and 0.865, respectively.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Regresyon Analizi · Makine Öğrenimi · Veri Madenciliği

Abstract

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.

Özet

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.

🔖

Cite This Article

Kaya, G., Şen, E., Altay, O. (2025). Predicting CPU Performance Score with Regression Analysis. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, Cilt: 16 Sayı: 1, 1–11. https://doi.org/10.24012/dumf.1493049

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