Table of Contents

- 1 How are linear and logistic regression similar?
- 2 What is the difference between the linear regression and logistic regression?
- 3 Is logistic regression classification or regression?
- 4 What are the differences and similarities if any between multiple lin ear regression models and logistic regression models?
- 5 What does logistic regression Tell Me?
- 6 What is the equation for logistic regression?

## How are linear and logistic regression similar?

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.

### What is the difference between the linear regression and logistic regression?

Linear regression is used to predict the continuous dependent variable using a given set of independent variables. Logistic Regression is used to predict the categorical dependent variable using a given set of independent variables.

**What are the commonalities and differences between regression and correlation?**

Correlation is a single statistic, or data point, whereas regression is the entire equation with all of the data points that are represented with a line. Correlation shows the relationship between the two variables, while regression allows us to see how one affects the other.

**Is logistic regression equivalent to linear regression?**

Linear Regression is all about fitting a straight line in the data while Logistic Regression is about fitting a curve to the data. Linear Regression is a regression algorithm for Machine Learning while Logistic Regression is a classification Algorithm for machine learning.

## Is logistic regression classification or regression?

Logistic regression is basically a supervised classification algorithm. In a classification problem, the target variable(or output), y, can take only discrete values for a given set of features(or inputs), X. Contrary to popular belief, logistic regression IS a regression model.

### What are the differences and similarities if any between multiple lin ear regression models and logistic regression models?

They are both parametric Regressions, and both utilize a linear equation to arrive at predictions. However, the similarities end there. In Linear regression the result is continuous. In Logistic Regression, there are only a limited number of possible values.

**What is the difference between Logistic regression and Cox regression?**

Cox proportional hazard risk model is a method of time-to-event analysis while logistic regression model do not include time variable. In such a situation, logistic regression will not reveal the benefits of the intervention in the study, while the Cox model does.

**What are the differences and similarities if any between multiple lin ear regression models and Logistic regression models?**

## What does logistic regression Tell Me?

Purpose and examples of logistic regression. Logistic regression is one of the most commonly used machine learning algorithms for binary classification problems,which are problems with two class values,including

### What is the equation for logistic regression?

Using the generalized linear model, an estimated logistic regression equation can be formulated as below. The coefficients a and bk (k = 1, 2., p) are determined according to a maximum likelihood approach, and it allows us to estimate the probability of the dependent variable y taking on the value 1 for given values of xk (k = 1, 2., p).

**What are the assumptions of logistic regression?**

Assumptions of Logistic Regression. This means that the independent variables should not be too highly correlated with each other. Fourth, logistic regression assumes linearity of independent variables and log odds. although this analysis does not require the dependent and independent variables to be related linearly,…

**What is the function of logistic regression?**

Logistic Regression uses the logistic function to find a model that fits with the data points. The function gives an ‘S’ shaped curve to model the data. The curve is restricted between 0 and 1, so it is easy to apply when y is binary.