Logistic regression is a statistical method used for predictive modeling. It is commonly used in data analysis and machine learning to determine the probability of an event occurring based on one or more predictor variables. The goal of logistic regression is to find the relationship between the independent variable(s) and the dependent variable, which can be binary (e.g., success/failure, yes/no), ordinal (e.g., low, medium, high), or continuous.
In a logistic regression model, the outcome variable is typically categorical, while the predictor variables are usually continuous. The model estimates the probability of an observation belonging to each category based on the values of the independent variables. This can be used for classification tasks such as predicting whether an email is spam or not, or for prediction tasks such as estimating the likelihood of a customer purchasing a product.
The logistic regression algorithm works by fitting a curve that represents the probability of the dependent variable given different values of the independent variables. The model can be used to make predictions on new data points by calculating their probability of belonging to each category based on the values of the predictor variables.