Logistic regression is a popular classification algorithm used in statistical modeling and machine learning to predict the probability of a binary outcome. Unlike linear regression, which predicts continuous values, logistic regression estimates the likelihood that a given input belongs to one of two predefined categories. The algorithm applies a logistic function to the input features, transforming the output into a probability score between 0 and 1. This probability can then be used to classify data into one of the two categories.

The strength of logistic regression lies in its simplicity and interpretability. It is particularly useful when the relationship between the dependent variable and the independent variables is not strictly linear but can be modeled through a sigmoid function. Logistic regression also provides valuable insights into the significance of each feature in predicting the outcome, as the coefficients of the model can be interpreted in terms of their impact on the probability of belonging to a particular class. Despite its simplicity, logistic regression performs well for many classification tasks and serves as a foundation for more complex algorithms.