Researchers at Pennsylvania State University examined whether machine learning could predict the risk and contributing ...

Multiple classification models including Logistic Regression, Linear SVM, and Random Forest were trained and evaluated. Among them, XGBoost performed best, achieving high accuracy and balanced ...

About Machine learning algorithm selector GUI: regression, classification, time series forecasting, clustering, dimensionality reduction, new data generation and special classification scenarios.

Scikit-learn library and Statistics and Machine Learning Toolbox within MathWorks were then used to perform unsupervised clustering and supervised regression learning. The impact of dataset ...

Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR ...

This paper presents a comprehensive machine learning approach for credit score classification, addressing key challenges in financial risk assessment. We propose an optimized CatBoost-based framework ...

This study introduces a novel machine learning framework integrating Clustering and Multi-target Classification to analyze employment waiting time and job linearity among women STEM alumni.