LOGISTIC REGRESSION Meaning and
Definition
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Logistic regression is a statistical modeling technique used to predict a categorical outcome variable based on one or more independent variables or predictors. It is a type of regression analysis that is suitable for modeling binary or dichotomous outcomes, where the dependent variable takes only two possible values, such as "yes" or "no," "success" or "failure," or "0" or "1." The main objective of logistic regression is to determine the relationship between the predictor variables and the probability of occurrence of a particular outcome.
Unlike linear regression, which predicts a continuous outcome variable, logistic regression estimates the probability of an event occurring based on the values of the predictor variables. The result is a function that maps the predictor values to a range between 0 and 1, representing the probability of the event occurring, where values closer to 1 indicate a higher probability.
Logistic regression utilizes the logistic function, also known as the sigmoid function, to model the relationship between the predictors and the probability of occurrence. The logistic function ensures that the predicted probabilities fall within the valid range, preventing unrealistic or impossible predictions.
The logistic regression model estimates the coefficients of the predictor variables, which represent the strength and direction of their influence on the outcome. These coefficients are often interpreted as odds ratios, indicating the multiplicative change in odds for the occurrence of the event associated with a unit change in the predictor variable.
Logistic regression is widely used in various fields, including medicine, social sciences, marketing, finance, and machine learning, for tasks such as disease prediction, customer churn analysis, credit risk assessment, and sentiment analysis, among others.
Etymology of LOGISTIC REGRESSION
The term "logistic regression" originated from the field of statistics and is a combination of two separate words: "logistic" and "regression".
"Regression" in statistics refers to a modeling technique used to investigate the relationship between a dependent variable (outcome) and one or more independent variables (predictors). It seeks to find the best fitting line or curve that describes the relationship between the variables.
The term "logistic" stems from the mathematical function called the "logistic function" or "sigmoid function". This function is commonly used in logistic regression to model the probability of the outcome variable taking a particular value. It transforms the linear regression output into a range between 0 and 1, allowing for the prediction of discrete outcomes or binary events.
Thus, the term "logistic regression" emphasizes the use of a logistic function within the framework of regression analysis to model categorical or binary outcomes.
