Contents
Do P values matter in logistic regression?
For binary logistic regression, the format of the data affects the p-value because it changes the number of trials per row. Deviance: The p-value for the deviance test tends to be lower for data that are in the Binary Response/Frequency format compared to data in the Event/Trial format.
How linear and logistic regression are different?
Linear regression is used for predicting the continuous dependent variable using a given set of independent features whereas Logistic Regression is used to predict the categorical. Linear regression is used to solve regression problems whereas logistic regression is used to solve classification problems.
What’s the difference between linear regression and logistic regression?
In Linear regression, it is required that relationship between dependent variable and independent variable must be linear. In Logistic regression, it is not required to have the linear relationship between the dependent and independent variable. In linear regression, there may be collinearity between the independent variables.
What should be the output of linear regression?
The output for Linear regression should only be the continuous values such as price, age, salary, etc. The relationship between the dependent variable and independent variable can be shown in below image:
When to use linear regression in continuous dependent variable?
It is used for predicting the continuous dependent variable with the help of independent variables. The goal of the Linear regression is to find the best fit line that can accurately predict the output for the continuous dependent variable.
How is linear regression used in machine learning?
Linear Regression: Linear Regression is one of the most simple Machine learning algorithm that comes under Supervised Learning technique and used for solving regression problems. It is used for predicting the continuous dependent variable with the help of independent variables.