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Regression Analysis in R: Linear Regression and Logistic Regression
This first chapter of the series of statistical data analysis using R, which the authors provides in this second part (PART II) of the book,...
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Linear and Logistic Regression
Logistic regression is a linear classification technique and a fundamental element of many machine-learning systems. This chapter introduces it as...
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Two improving approaches for faulty interaction localization using logistic regression analysis
Faulty Interaction Localization (FIL) is a process to identify which combination of input parameter values induced test failures in combinatorial...
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Principal Components Analysis Based Imputation for Logistic Regression
The field of AI and machine learning is constantly evolving, and as the size of data continues to grow, so does the need for accurate and efficient...
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Bayesian analysis for matrix-variate logistic regression with/without response misclassification
Matrix-variate logistic regression is useful in facilitating the relationship between the binary response and matrix-variates which arise commonly...
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Logistic Regression
Please download the sample Excel files from for this chapter’s exercises.
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A Study Based on Logistic Regression Algorithm to Teaching Indicators
Objective: This study aims to examine the factors that influence teachers’ choice regarding the importance of instructional indicators. Methods:...
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EPoLORE: Efficient and Privacy Preserved Logistic Regression Scheme
Logistic regression, as one of the classification method, is widely used in machine learning. Due to the complexity of training process, outsourcing...
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Logistic regression prediction models and key influencing factors analysis of diabetes based on algorithm design
This article focuses on the key influencing factors and prediction accuracy of diabetes. Nine test indexes were mainly considered: low density...
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Regression Analysis
Regression is the process of learning relationships between inputs and continuous outputs from data. In this chapter, we will discuss and analyze...
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Using Logistic Regression Approach to Predicating Breast Cancer DATASET
The aim of this study is to predicate breast cancer by using three approaches: Multilayer perceptron, multiple linear regression, and logistic...
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Regression Analysis
In this chapter, we introduce regression analysis andRegression analysis some of its applications in data scienceData science. Regression is related...
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Regression Analysis
Regression is one of the well-understood and studied algorithms in statistics. It has been successfully applied to machine learning and has been...
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PEVLR: A New Privacy-Preserving and Efficient Approach for Vertical Logistic Regression
In our paper, we consider logistic regression in vertical federated learning. A new algorithm called PEVLR (Privacy-preserving and Efficient Vertical...
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Obesity Level Prediction Using Multinomial Logistic Regression
With the increase in remote jobs and also due to the present ongoing pandemic for the past 1 year, most of the IT companies have taken a decision and...
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Development of Intrusion Detection Using Logistic Regression with Various Preprocessing Approaches
Preprocessing is very important to predict Intrusion Detection System (IDS) with respect to any parameters. It entails prep** and converting raw...
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Adaptive hypergraph regularized logistic regression model for bioinformatic selection and classification
The classification of cancer using established biological knowledge has become increasingly prevalent, primarily due to the improved accuracy and...
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Classification of Heart Diseases Using Logistic Regression with Various Preprocessing Techniques
Machine learning (ML) based heart disease prediction has emerged as a crucial and fruitful field of study and application. They are used to analyze...
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Logistic Regression Analysis
This chapter covers the logistic regression concept and implementation in a structured way. Preceding chapters introduced supervised learning and...
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A general robust low–rank multinomial logistic regression for corrupted matrix data classification
Multi-classification of corrupted matrix data is a significant problem in machine learning and pattern recognition. However, most of the existing...
