Explore how Logistic Regression extends to multi-class problems using One-vs-Rest (OvR) and Softmax Regression. Learn about coefficient updates with gradient descent, one-hot encoding, and categorical cross-entropy loss for accurate predictions.

Explore logistic regression, a powerful classification algorithm, from its basic geometric principles like decision boundaries and half-planes, to its use of the sigmoid function for probabilistic predictions. Understand why maximum likelihood estimation and binary cross-entropy loss are crucial for finding the optimal model in classification tasks. Learn how distance from the decision boundary translates to prediction confidence.