📚 Logistic Regression & Probability Translators
These references reinforce the storyline behind The Probability Translator by covering the math behind the sigmoid function, practical implementation guides, and recommended follow-up material for decision thresholds and interpretability.
Table of Contents
- Foundational Theory
- Hands-On Guides & Tooling
- Decision Thresholds & Metrics
- Interpretability & Clinical Context
1. Foundational Theory
| Resource | Type | Notes | Access |
|---|---|---|---|
| An Introduction to Statistical Learning (2nd ed.) | Textbook | Chapter 4 introduces logistic regression, maximum likelihood estimation, and medical case studies. | https://www.statlearning.com/ |
| The Elements of Statistical Learning | Textbook | Chapter 4 details generalized linear models and regularized logistic regression. | https://hastie.su.domains/ElemStatLearn/ |
| Pattern Recognition and Machine Learning by C. Bishop | Textbook | Section 4.3 develops logistic regression from a probabilistic perspective. | https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/ |
| Machine Learning by T. Mitchell | Textbook | Chapter 6 explains the log-likelihood objective and gradient updates. | http://www.cs.cmu.edu/~tom/mlbook.html |
2. Hands-On Guides & Tooling
| Resource | Focus | Language |
|---|---|---|
| Scikit-learn User Guide – Logistic Regression | Regularization, solver comparison, multiclass strategies. | 🇬🇧 English |
| Statsmodels GLM Documentation – Logit | GLM formulation, link functions, inference outputs. | 🇬🇧 English |
| scikit-learn Tutorial: Classification | End-to-end example translating feature engineering into predictions. | 🇬🇧 English |
| Google ML Crash Course – Logistic Regression | Sigmoid intuition, loss, regularization, short exercises. | 🇬🇧 English |
3. Decision Thresholds & Metrics
| Resource | Why it matters | Access |
|---|---|---|
| Google ML Crash Course – Thresholds and the Confusion Matrix | Visualizes how threshold tuning affects true/false positives, mirroring the Risk Manager storyline. | 🇬🇧 English |
| Google ML Crash Course – Accuracy, Precision, Recall | Clarifies evaluation metrics used after translating probabilities into decisions. | 🇬🇧 English |
| scikit-learn Model Evaluation Guide | Covers ROC curves, calibration, and threshold-dependent metrics. | 🇬🇧 English |
| ROC and AUC Explained | Connects ROC analysis to the chapter’s emphasis on balancing risk. | 🇬🇧 English |
4. Interpretability & Clinical Context
| Resource | Highlight | Language |
|---|---|---|
| IBM Think – What Is Logistic Regression? | Business and healthcare applications, odds interpretation, overfitting risks. | 🇬🇧 English |
| World Health Organization – Risk Prediction Models | Discusses logistic models for medical decision support, aligning with the clinic narrative. | 🇬🇧 English |
| Nature Digital Medicine – Predicting Hospital Readmissions | Case study showing logistic regression in healthcare resource planning. | 🇬🇧 English |
Note: All links were re-checked in October 2025. For licensed content, consult institutional libraries or open-access repositories.