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In this way, what is meant by logistic regression?

Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes).

Additionally, what is loss function in logistic regression? Loss function for Logistic Regression The loss function for linear regression is squared loss. The loss function for logistic regression is Log Loss, which is defined as follows: Log Loss = ∑ ( x , y ) ∈ D − y log ? ( y ′ ) − ( 1 − y ) log ? where: ( x , y ) ∈ D.

One may also ask, what is meant by regression in machine learning?

Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting.

What is the difference between linear and logistic regression?

The essential difference between these two is that Logistic regression is used when the dependent variable is binary in nature. In contrast, Linear regression is used when the dependent variable is continuous and nature of the regression line is linear.