Decision tree

As explained in Inside AI

A decision tree is a hierarchical predictive model that recursively splits data according to attribute-based questions to reach decisions or predictions.

A decision tree turns labeled data into a sequence of attribute-based questions. Each answer selects a branch, and a final leaf supplies a category or decision. section 3.1

The tree’s structure is inferred from data. Attribute values form branches, while mathematical measures help place the most discriminative questions near the beginning so decisions can be reached efficiently. section 3.1 section 3.2

Real-time credit-card fraud prevention

A bank can analyze 18 months of labeled transactions using purchase amount, timing, merchant type, location, and card presence. If international transactions show substantially more fraud, the first node asks whether a transaction is international or domestic. Each branch then receives further questions, producing an accept-or-decline assessment. section 3.2

Figure 3.1 The first few levels of a decision tree. In a real-world application, a full tree may contain thousands of nodes.

Shows the first levels of a decision tree and helps visualize how successive questions create branches toward terminal decisions. section 3.2

How Inside AI covers it

How deep it goes

Explains nodes, branches, leaves, recursive partitioning, and attribute ranking, but does not provide the underlying mathematical formulas.

Examples

Uses animal classification and credit-card fraud detection to demonstrate question ordering, branching, and final decisions.

What it assumes

Assumes labeled data and basic classification while teaching tree terminology and construction from attribute-based questions.

What you can do after

Build it from scratch by ranking transaction attributes, recursively partitioning labeled records, and assigning decisions to terminal branches.

How it connects

Connects data mining, classification, labeled data, and fraud prevention.

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  Demand Forecasting Best PracticesA decision tree is a machine-learning model that makes predictions by applying a sequence of feature-based questions and following the resulting branches to a terminal prediction.
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Related concepts

• leaf nodehas part · A leaf node is a node in a tree or hierarchical structure that has no child nodes and cannot be further subdivided.
• random forestpart of · A random forest is an ensemble model that combines predictions from multiple decision trees built with randomized data samples, feature selections, or both.
• language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• decision nodehas part · A decision node is a tree node where a decision, test, or action selection determines which successor branch is followed.
• decision stumpincludes · A decision stump is a decision tree of depth one with a single decision node and prediction leaves.
• XGBoostused by · XGBoost is an open-source software library for efficient gradient-boosted decision-tree algorithms used in supervised machine learning.

This explainer was generated by Manning's knowledge graph from the text of Inside AI. The section links point to the parts of the book it draws on.