Logistic Regression

Logistic regression bends a straight line into an S-curve to answer yes-or-no questions with probabilities.

Build 1.5 hr Python

Prerequisites: Phase 2 Lesson 1-2 (What Is ML, Linear Regression)

#Learning Objectives

Implement logistic regression from scratch using the sigmoid function and binary cross-entropy loss
Compute and interpret precision, recall, F1 score, and the confusion matrix for binary classification
Explain why MSE fails for classification and why binary cross-entropy produces a convex cost surface
Build a softmax regression model for multi-class classification and evaluate threshold tuning tradeoffs

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.