"A regression equation used to predict a binary variable." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)
"A regression model where the dependent variable takes on a limited number of discrete values, often two values representing yes and no." (Peter L Stenberg & Mitchell Morehart, "Characteristics of Farm and Rural Internet Use in the USA", 2008)
"Technique for making predictions when a dependent variable is a categorical dichotomy, and the independent variable(s) are continuous and/or categorical." (Ken J Farion et al, "Clinical Decision Making by Emergency Room Physicians and Residents", 2008)
"A form of regression analysis in which the target variable (response variable) is a binary-level or ordinal-level response and the target estimate is bounded at the extremes." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)
"A modeling technique where unknown values are predicted by known values of other valuables where the dependent variable is binary type." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
"Logistic regression is a method of statistical modeling appropriate for categorical outcome variables. It describes the relationship between a categorical response variable and a set of explanatory variables." (Leping Liu & Livia D’Andrea, "Initial Stages to Create Online Graduate Communities: Assessment and Development", 2011)
"Like linear regression, a statistical method of modeling the relationship between dependent and independent variables based on probability. However, in binary logistic regression, the dependent variable (the effect, or outcome) can have only one of two values, as in, say, a baby’s sex or the results of an election. (Multinomial logistic regression allows for more than two possible values.) A logistic regression model is formed by fitting data to a logit function. (The dependent variable is a 0 or 1, and the regression curve is shaped something like the letter 's'.) market basket analysis: The identification of product combinations frequently purchased within a single transaction." (Meta S Brown, "Data Mining For Dummies", 2014)
"Logistic regression is a statistical method for determining the relationship between independent predictor variables (such as financial ratios) and a dichotomously coded dependent variable (such as default or non-default)." (Niccolò Gordini, "Genetic Algorithms for Small Enterprises Default Prediction: Empirical Evidence from Italy", 2014)
"Logistic regression is a predictive analytic method for describing and explaining the relationships between a categorical dependent variable and one or more continuous or categorical independent variables in the recent and past existing data in efforts to build predictive models for predicting a membership of individuals or products into two groups or categories." (Sema A Kalaian & Rafa M Kasim, "Predictive Analytics", 2015)
"Form of regression analysis where the dependent variable is a category rather than a continuous variable. An example of a continuous variable is sales or profit. In order to understand customer retention, regression analysis would calculate the effects of variables such as age, demographics, products purchased, and competitor information on two categories: retaining the customer and losing the customer." (Brittany Bullard, "Style and Statistics", 2016)
"A regression model that is used when the dependent variable is qualitative and a probability is assigned to an observation for the likelihood that the target variable has a value of 1." (Alan Olinsky et al, Visualization of Predictive Modeling for Big Data Using Various Approaches When There Are Rare Events at Differing Levels, 2018)
"Logistic regression analysis is mainly used in epidemiology. The most common case is to explore the risk factors of a certain disease and predict the probability of the occurrence of a certain disease according to the risk factors." (Chunfa Xu et al, "Crime Hotspot Prediction Using Big Data in China", 2020)
"Logistic regression is a classification algorithm that comes under supervised learning and is used for predictive learning. Logistic regression is used to describe data. It works best for dichotomous (binary) classification." (Astha Baranwal et al, "Machine Learning in Python: Diabetes Prediction Using Machine Learning", 2020)
"Logistic regression is a statistical technique for modeling the probability of an event. In a machine learning context, logistic regression refers to a classification model based on this statistical technique." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)
"This is a kind of regression analysis often used when the outcome variable is dichotomous and scored 0, 1. Logistic regression is also known as logit regression and when the dependent variable has more than two categories it is called multinomial. Logistic regression is used when predicting whether an event will happen or not." (John K Rugutt & Caroline C Chemosit, "Student Collaborative Learning Strategies: A Logistic Regression Analysis Approach", 2021)
No comments:
Post a Comment