Logistic Regression

Logistic regression is a linear classification model that computes a weighted sum of input features, passes the result through a sigmoid or softmax function, and outputs class probabilities. It is one of the most widely used classical baselines for text classification because it is fast to train, interpretable through its feature weights, and regularizable to handle high-dimensional sparse feature spaces like TF-IDF vectors. Its linear decision boundary is a limitation for tasks with complex feature interactions.