// ◇ Classical Machine Learning
Logistic Regression
Turns a linear score into a probability with a sigmoid for binary classification.
逻辑回归 1958 Beginner core
IN PLAIN WORDS
Compute a linear score, squash it into 0–1 with a sigmoid, and train with cross-entropy. A neural network's final softmax layer is its multi-class generalization.
Used in: Credit scoring, CTR prediction, clinical risk models
TIMELINE · prerequisites → this → what builds on it
01Original paper
The Regression Analysis of Binary Sequences
Cox et al. · Journal of the Royal Statistical Society Series B: Statistical Methodology · 1958
DOI ↗ ✓ Verified · Crossref API doi:10.1111/j.2517-6161.1958.tb00292.x
02Where it sits
Learn the left column first; the right column builds on this one.
Learn first
Related:Perceptron
Left: prerequisites · right: builds on this · dashed: related. Full map →
03Learning resources
Official docs, explainers, courses and reference code. All links checked.
04Where the course teaches it
On learning paths:Zero to Deep Learning
For AI / data
- Verification: verified, checked 2026-10-21.
- JSON: /api/learn/algorithms.json (id:
logistic-regression)