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The Boundary of Choice: Logistic Regression from First Principles
Paperback
Series: Patterns Before Predictions a Fear-Free Machine Learning Journey from First Principles, Book 3
General EducationGeneral Mathematics
Publisher: Independently Published
Published: Aug 2 2026
Pages: 326
Weight: 0.87
Height: 0.81 Width: 6.00 Depth: 9.00
Language: English
Will a customer respond? Should a machine be inspected? Is a transaction unusual? Does a learner need timely support? These questions do not ask for an exact number. They ask us to weigh imperfect clues, estimate possibility, and decide where action should begin.
Step by step, the book reveals the full decision chain: features become weighted evidence; weighted evidence becomes a score; the logistic curve turns that score into a probability; and a threshold turns probability into a class or action. Every mathematical idea arrives only after its human purpose is clear.
Inside this book, you will learn how to:
- distinguish a number-prediction problem from a classification problem;
- choose useful features and understand what a model can-and cannot-see;
- interpret weights, the intercept, raw scores, probability, odds, and log-odds;
- understand the logistic or sigmoid curve without beginning with intimidating notation;
- set decision thresholds and examine false positives, false negatives, precision, recall, calibration, and workload;
- build a transparent logistic classifier by hand, one calculation at a time;
- see how error, loss, gradients, learning rate, repetition, validation, and regularisation help a model learn;
- recognise overfitting, class imbalance, data leakage, unstable features, bias, distribution shift, and unsuitable uses;
- apply the ideas responsibly in customer work, maintenance, education, healthcare, finance, agriculture, and operations;
- create a model card, evaluation plan, monitoring loop, and human-review policy for a small classification system.
No programming experience is required. No confidence in advanced mathematics is assumed. The book does not treat probability as certainty or a model score as permission. Fairness, monitoring, human judgement, limits, and accountability are woven into the learning journey from the beginning.
By the final page, logistic regression will no longer feel like a mysterious equation. You will be able to explain why the method exists, how it turns clues into probabilities, how thresholds change decisions, where errors enter, when the model fits the problem, when it fails, and when the wisest choice is not to automate at all.
Start with one ordinary question. Follow the evidence. Find the boundary. Learn to choose with clarity.
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