What is logistic regression?
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Logistic regression is a statistical method used for binary classification problems, where the outcome is one of two possible categories. It models the relationship between a dependent binary variable and one or more independent variables by estimating probabilities using a logistic (sigmoid) functiRead more
Logistic regression is a statistical method used for binary classification problems, where the outcome is one of two possible categories. It models the relationship between a dependent binary variable and one or more independent variables by estimating probabilities using a logistic (sigmoid) function. The output is a probability value that is mapped to one of the two possible classes using a threshold, typically 0.5.
In logistic regression, the model predicts the log-odds of the dependent variable being in a particular category. The log-odds are a linear combination of the independent variables.
Where:
– \( p \) is the probability of the dependent variable being 1.
– \( \beta_0 \) is the intercept.
– \( \beta_1, \beta_2, \ldots, \beta_n \) are the coefficients of the independent variables \( X_1, X_2, \ldots, X_n \).
Logistic regression is widely used in fields such as medicine, finance, and social sciences for tasks like disease prediction, credit scoring, and survey analysis.
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