Logistic regression

PrepNuggets

LEVEL II

Logistic regression is a statistical method used for binary classification problems. The goal of logistic regression is to model the probability of a certain class or event occurring given a set of input features. It does this by using a logistic function, which is a sigmoid function that outputs a probability value between 0 and 1.

The logistic regression model makes the assumption that the log-odds of the outcome are a linear combination of the input features. It’s important to note that the Logistic Regression model is only suitable when the response variable is binary (0/1, True/False) and assumes that the relationship between the predictor variables and the log-odds of the response variable is linear.

Compare: Dummy variable

Also Known As:
Logit model
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