Logistic Regression

Extend linear models to classification. The sigmoid function squashes outputs to probabilities, and cross-entropy loss measures prediction quality.

Learning Objectives

• →Apply the sigmoid activation function
• →Derive binary cross-entropy loss
• →Compute gradients for logistic models
• →Threshold probabilities for classification

Practice