In Table 3, the VIF values for each variable are < 5, which has been reduced as multicollinearity between variables. 3.3. Use the Entropy Weight Method to Weight the Data When exploring the factors ...

This study compared the performance of traditional logistic regression and machine learning models in predicting adult sepsis mortality. Objective: To develop an optimum model for predicting the ...

This project predicts CO2 emissions using Linear and Logistic Regression. It involves data analysis, preprocessing, and model implementation in Python. Deliverables include a dataset, code notebook, ...

Have you ever found yourself staring at a spreadsheet, trying to make sense of all those numbers? Many face the challenge of transforming raw data into actionable insights, especially when it comes to ...

This article presents a complete demo program for logistic regression, using batch stochastic gradient descent training with weight decay. Compared to other binary classification techniques, logistic ...

Learn how to perform regression analysis of your data in Microsoft Excel, a statistical process that helps you understand the relationship ...

In modern era, the Machine learning especially Supervised Machine Learning is iridescent field where many researchers are shaping their research work. Innovations and new ideas in field are growing ...

Abstract: Sparse logistic regression is for classification and feature selection simultaneously. Although many studies have been done to solve $\ell_{1}$-regularized logistic regression, there is no ...