What is logistic regression in Machine learning?

Question

Can anyone explain logistic regression in Machine learning?

Answer 1

Logistic regression in Machine Learning is a supervised algorithm and the most used classification algorithm. Logistic regression predicts the probability of occurrence of the event using a log of odds as the dependent variable. Logistic regression uses a link function called sigmoid function to bring the target variable to 0 to 1 to get the probability of occurrence of an event. In the case of binary classification, if the probability is more than the threshold, we categorize one class otherwise another class.

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Answer 2

Logistic regression is a classification algorithm used in Machine Learning that works based upon the supervised learning architecture. Here, the probability of a target variable is predicted using the inputs. In classification, the outcome is usually binary, with 1 implying success and 0 implying failure. In mathematical terms, however, logistic regression is an operation that predicts P(y=1) as a function of a variable 'x.' It is a simple Machine Learning algorithm that is used to perform a variety of classification operations. There are many types of logistic regression, such as binomial, ordinal, and multinomial regressions. As stated above, the simplest one to use is the binary logistic regression.

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