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Logistic regression - Wikipedia
In regression analysis, logistic regression[1] (or logit regression) estimates the parameters of a logistic model (the coefficients in the linear or non linear combinations).

Logistic Regression in Machine Learning - GeeksforGeeks
Logistic Regression is a supervised machine learning algorithm used for classification problems. Unlike linear regression which predicts continuous values it predicts the probability that an input belongs to a specific class.

What is logistic regression? - IBM
Logistic regression, like linear regression, is a type of linear model that examines the relationship between predictor variables (independent variables) and an output variable (the response, target or dependent variable).

12.1 - Logistic Regression | STAT 462 - Statistics Online
Logistic regression helps us estimate a probability of falling into a certain level of the categorical response given a set of predictors. We can choose from three types of logistic regression, depending on the nature of the categorical response variable:

Introduction to Logistic Regression - Statology
This tutorial provides a simple introduction to logistic regression, one of the most commonly used algorithms in machine learning.

Logistic Regression Overview with Example - Statistics by Jim
Unlike linear regression, logistic regression focuses on predicting probabilities rather than direct values. It models how changes in independent variables affect the odds of an event occurring.

What is Logistic Regression? A Beginner's Guide [2025]
What is logistic regression and what is it used for? What are the different types of logistic regression? Discover everything you need to know in this guide.

 

 

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(PDF) Logistic regression in data analysis: An overview  researchgate.net

The proper application of logistic regression model in complex survey data: a systematic review  BMC Medical Research Methodology

Comparative analysis of heart disease prediction using logistic regression, SVM, KNN, and random forest with cross-validation for improved accuracy  Nature

Risk factor analysis for stunting incidence using sparse categorical principal component logistic regression  ScienceDirect.com

Risk factors associated with the failure of day surgery for total knee arthroplasty a multivariate logistic regression analysis  Frontiers

Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci  Nature

A cross-sectional study comparing machine learning and logistic regression techniques for predicting osteoporosis in a group at high risk of cardiovascular disease among old adults  BMC Geriatrics

Applying Logistic Regression Model in Oracle Analytics  Oracle Blogs

Analysis of outlier villages with high under-five mortality rates in Malawi using mixed-effects logistic regression model residuals  BMC Public Health

A grip strength prediction tool for older adults based on logistic regression: construction, validation, and clinical application value  Nature

Exploring factors influencing childhood immunization status in East Africa using multilevel ordinal logistic regression analysis  Frontiers

Evaluation and analysis of risk factors for fractured vertebral recompression post-percutaneous kyphoplasty: a retrospective cohort study based on logistic regression analysis  BMC Musculoskeletal Disorders

Logistic regression analysis of risk factors for anxiety and depression in patients with coronary heart disease and subclinical hypothyroidism  Nature

Risk factors analysis of cognitive frailty among geriatric adults in nursing homes based on logistic regression and decision tree modeling  Frontiers

Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors  BMC Health Services Research

 

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