Can you use logistic regression for regression problems?

Since both are part of a supervised model so they make use of labeled data for making predictions. Linear regression is used for regression or to predict continuous values whereas logistic regression can be used both in classification and regression problems but it is widely used as a classification algorithm.
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Is logistic regression used to perform regression?

Logistic regression is a statistical method used to predict the outcome of a dependent variable based on previous observations. It's a type of regression analysis and is a commonly used algorithm for solving binary classification problems.
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Should I use logistic regression or linear regression?

Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems. Linear regression provides a continuous output but Logistic regression provides discreet output.
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Why can't a linear regression be used instead of logistic regression?

This article explains why logistic regression performs better than linear regression for classification problems, and 2 reasons why linear regression is not suitable: the predicted value is continuous, not probabilistic. sensitive to imbalance data when using linear regression for classification.
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When should logistic regression not be used?

4. Logistic Regression should not be used if the number of observations is lesser than the number of features, otherwise, it may lead to overfitting.
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StatQuest: Logistic Regression



What types of problems are best suited for logistic regression?

Logistic regression is a powerful machine learning algorithm that utilizes a sigmoid function and works best on binary classification problems, although it can be used on multi-class classification problems through the “one vs. all” method. Logistic regression (despite its name) is not fit for regression tasks.
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What can logistic regression be used for?

Similar to linear regression, logistic regression is also used to estimate the relationship between a dependent variable and one or more independent variables, but it is used to make a prediction about a categorical variable versus a continuous one.
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Can logistic regression be used for non linear data?

So to answer your question, Logistic regression is indeed non linear in terms of Odds and Probability, however it is linear in terms of Log Odds.
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What is the difference between logistic regression and multiple regression?

Simple logistic regression analysis refers to the regression application with one dichotomous outcome and one independent variable; multiple logistic regression analysis applies when there is a single dichotomous outcome and more than one independent variable.
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Is logistic regression mainly used for regression True or false?

2) True-False: Is Logistic regression mainly used for Regression? Logistic regression is a classification algorithm, don't confuse with the name regression.
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What is the difference between Linear Regression and logistic regression explain using example?

In linear regression the outcome is continuous whereas in logistic regression, the outcome has only a limited number of possible values(discrete). example: In a scenario,the given value of x is size of a plot in square feet then predicting y ie rate of the plot comes under linear regression.
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What type of regression is a logistic regression?

Logistic regression is a supervised learning algorithm used to predict a dependent categorical target variable.
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What is better than logistic regression?

For identifying risk factors, tree-based methods such as CART and conditional inference tree analysis may outperform logistic regression.
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Is logistic regression A multivariate analysis?

Multivariate Analysis: Logistic Regression.
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How many variables should be in a logistic regression model?

There must be two or more independent variables, or predictors, for a logistic regression. The IVs, or predictors, can be continuous (interval/ratio) or categorical (ordinal/nominal).
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Is logistic regression always linear?

The short answer is: Logistic regression is considered a generalized linear model because the outcome always depends on the sum of the inputs and parameters. Or in other words, the output cannot depend on the product (or quotient, etc.) of its parameters!
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Is logistic regression considered linear model?

Logistic regression is considered a linear model because the features included in X are, in fact, only subject to a linear combination when the response variable is considered to be the log odds. This is an alternative way of formulating the problem, as compared to the sigmoid equation.
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Can the logistic regression model classify any non linearity?

Actually, you can get a non-linear decision boundary with only one layer having an activation. See the standard example of an XOR with a 2-layer feed-forward network. Logistic regression is neither linear nor is it a classifier.
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Can we use logistic regression for classification?

Logistic regression is a simple yet very effective classification algorithm so it is commonly used for many binary classification tasks.
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Can logistic regression be used for multiclass classification problems?

Logistic regression, by default, is limited to two-class classification problems. Some extensions like one-vs-rest can allow logistic regression to be used for multi-class classification problems, although they require that the classification problem first be transformed into multiple binary classification problems.
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Can we use logistic regression for continuous variables?

Logistic regression is usually used with binary response variables ( 0 or 1 ), the predictors can be continuous or discrete.
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Which is better logistic regression or decision tree?

If you've studied a bit of statistics or machine learning, there is a good chance you have come across logistic regression (aka binary logit).
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What is logistic regression how logistic regression is useful for classification problems?

Logistic Regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The intention behind using logistic regression is to find the best fitting model to describe the relationship between the dependent and the independent variable.
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