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What is the Logistic Regression?

Categorical variable

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Logistic regression is a commonly used classification algorithm in machine learning that allows categorizing data into discrete classes. It learns a linear relationship from a given dataset and introduces nonlinearity through an activation function to determine a hyperplane that separates the learning points into subclasses . Logistic regression is a type of generalized linear model that is applied when the outcome variable is not measured on a continuous scale. It is used for settings where the outcome variable is categorical, such as binomial, multinomial, and ordinal logistic regression models . Logistic regression is a statistical method used to analyze the correlation between categorical response variables and predictor variables, which can be categorical or continuous. It assumes a linear relationship between the predictor variable and its logit link function, but in some cases, this assumption may be incorrect, and the relationship can be quadratic, cubic, or in other forms. In such cases, bivariate polynomial ordinal logistic regression (BPOLR) models can be used to model the relationship as a polynomial form . Logistic regression is closely related to machine learning and is used for binary data categorization. It is applicable to multiple dependent variables and is used when the data used or given is irregular or of rare occurrences .

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Open access•Journal Article•DOI Parameter Estimation and Hypothesis Testing of The Bivariate Polynomial Ordinal Logistic Regression Model Marisa Rifada, Vita Ratnasari, Purhadi Purhadi - Show less +2 more 21 Jan 2023-Mathematics	Logistic regression is a statistical method used to analyze the correlation between categorical response variables and predictor variables.
Open access•Book Chapter•DOI Logistic Regression Emmanouil Brilakis 01 Jan 2023	Logistic regression is a type of generalized linear model used when the outcome variable is not measured on a continuous scale.
Open access•Journal Article•DOI Two Statistical Approaches to Justify the Use of the Logistic Function in Binary Logistic Regression Abdelhamid Zaidi, Asamh S.M. Al Luhayb - Show less +1 more 20 Apr 2023-Mathematical Problems in Engineering 1 Citations	Logistic regression is a classification algorithm that uses a linear relationship and a logistic function to categorize data into discrete classes.

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What are the advantageous of logistic regression?5 answersLogistic regression has several advantages. It allows for the prediction of probabilities for a specific event based on known or forecasted conditions, making it useful for risk assessment and decision-making. Logistic regression is particularly useful in medical research as it can investigate the independent effect of variables on a binomial outcome, adjust for multiple predictors, and reduce potential bias in observational data. It also helps in obtaining odds ratios and analyzing the association of all variables together, thus avoiding confounding effects. In ecological modeling, logistic regression is commonly used, but its additive structure assumes compensatory relationships between predictors. To address this, the min-linear logistic regression model was introduced, which has a built-in minimum structure of competing factors and performs well in identifying limiting factors.

What is a logistic regression?5 answersLogistic regression is a statistical method used to analyze the relationship between categorical response variables and predictor variables, which can be categorical or continuous. It is commonly used in machine learning for classification tasks, where it categorizes data into discrete classes based on a given set of labeled data. Logistic regression models the relationship between the predictor variables and the log-odds of the outcome variable using a logistic function, which transforms any real number into a probability between 0 and 1. It can be applied to binary outcomes or extended to handle multiple dependent variables. The goal of logistic regression is to estimate the probability of an event of interest based on the independent variables. Hypothesis testing methods, such as the Wald's test and likelihood ratio test, can be used to assess the significance of the regression coefficients.

What are the linear and logistic regression?3 answersLinear regression is a supervised learning technique used for mapping input variables to continuous output variables. It is used when the outcome variable is a real number and follows a linear relationship with the input variables. Linear regression can be performed in one variable or multiple variables, and it involves techniques such as gradient descent and polynomial regression. On the other hand, logistic regression is a modeling technique used for binary outcomes, where the outcome variable takes values such as yes/no or 1/0. Logistic regression is commonly used in various domains, including public health, finance, and marketing, to predict the likelihood of an event occurring based on input variables.

What is multivariate logistic regression?4 answersMultivariate logistic regression is a statistical method used to predict the relationship between multiple independent variables and a categorical dependent variable. It is an extension of the logistic regression model, which is used for predicting a binary outcome. Multivariate logistic regression allows for the inclusion of multiple independent variables, which can be either quantitative or qualitative. The model estimates the parameters associated with each independent variable and tests the overall effect of the group of independent variables on the dependent variable. It is used in various fields, such as healthcare, finance, and social sciences, to analyze and predict outcomes based on multiple factors. Several methods and algorithms have been developed to estimate the parameters and evaluate the performance of the multivariate logistic regression model.

What are the advantages and disadvantages of logistic regression?5 answersLogistic regression has several advantages. It is a useful technique for analyzing the relationship between predictor variables and a binary outcome. It allows for the estimation of odds ratios, which provide a meaningful interpretation of the impact of each variable on the odds of the observed event. Logistic regression also helps in avoiding confounding effects by analyzing the association of all variables together. However, there are also limitations to logistic regression. Overparameterized models with too many variables for too few events can result in implausibly large odds ratios and uninterpretable confidence intervals. Additionally, logistic regression assumes certain assumptions such as independence of errors, linearity in the logit, absence of multicollinearity, and an adequate number of events per independent variable. It is important to carefully select independent variables, ensure relevant assumptions are met, and choose an appropriate model building strategy when using logistic regression.

How to create a logistic regression model?16 answers

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