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The Tree of Decisions: Decision Trees from First Principles
Paperback
Series: Patterns Before Predictions a Fear-Free Machine Learning Journey from First Principles, Book 6
Publisher: Independently Published
Published: Aug 2 2026
Pages: 244
Weight: 0.66
Height: 0.61 Width: 6.00 Depth: 9.00
Language: English
Should this request be handled now or later? Does this case need human review? Which detail changes the next step?
The Tree of Decisions turns those familiar moments into a gentle, visual introduction to one of the most understandable ideas in artificial intelligence: the decision tree.
When the mathematics arrives, it has a purpose. Counts, proportions, Gini impurity, entropy, weighted averages, information gain, error, depth, validation, overfitting, and pruning are explained from first principles in plain language. No programming experience is required.
But a readable model is not automatically a trustworthy model. The book also examines biased labels, proxy variables, data leakage, unstable thresholds, distribution shift, uncertain cases, and the need for human oversight. Applications in customer support, education, healthcare support, finance, agriculture, manufacturing, and public services show both the value and the limits of tree-based reasoning.
Inside, readers will:
- build a complete decision tree by hand;
- follow new cases from root to leaf;
- compare candidate splits;
- understand why deeper is not always better;
- learn when to use, adapt, or avoid a decision tree; and
- practise with recall maps, chunking exercises, a capstone project, a glossary, and a thirty-day plan.
If artificial intelligence has ever felt distant, mathematical, or difficult to question, this book offers a calmer starting point: one honest question, one branch, and one layer of understanding at a time.
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