Two Statistical Approaches to Justify the Use of the Logistic Function in Binary Logistic Regression

Logistic regression is a commonly used classification algorithm in machine learning. It allows categorizing data into discrete classes by learning the relationship from a given set of labeled data. It learns a linear relationship from the given dataset and then introduces nonlinearity through an act...

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Bibliographic Details
Published in:Mathematical problems in engineering 2023-01, Vol.2023 (1)
Main Authors: Zaidi, Abdelhamid, Al Luhayb, Asamh Saleh M.
Format: Article
Language:English
Subjects:
Algorithms
Cardiovascular disease
Classification
Computer science
Computer simulation
Datasets
Heart
Hyperplanes
Machine learning
Mathematical functions
Neural networks
Random variables
Real numbers
Regression
Regression analysis
Statistical analysis
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Summary:Logistic regression is a commonly used classification algorithm in machine learning. It allows categorizing data into discrete classes by learning the relationship from a given set of labeled data. It learns a linear relationship from the given dataset and then introduces nonlinearity through an activation function to determine a hyperplane that separates the learning points into two subclasses. In the case of logistic regression, the logistic function is the most used activation function to perform binary classification. The choice of logistic function for binary classifications is justified by its ability to transform any real number into a probability between 0 and 1. This study provides, through two different approaches, a rigorous statistical answer to the crucial question that torments us, namely where does this logistic function on which most neural network algorithms are based come from? Moreover, it determines the computational cost of logistic regression, using theoretical and experimental approaches.
ISSN:1024-123X
1563-5147
DOI:10.1155/2023/5525675