Decision tree

As explained in Demand Forecasting Best Practices

A 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.

How Demand Forecasting Best Practices covers it

How deep it goes

Explains nodes, leaves, splits, and error-minimizing questions, while stopping short of detailed growth algorithms.

Examples

Uses Guess Who? and a demand dataset involving brand, price, promotion, and historical demand.

What it assumes

Assumes a dataset with features and outputs; teaches nodes, leaves, splits, and prediction logic from basic examples.

What you can do after

Evaluate decision-tree paths by identifying features, splits, terminal predictions, and the rationale for question selection.

How it connects

Connects machine learning with features, prediction error, forecasting, nodes, leaves, and splits.

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Also explained in

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  Grokking Machine LearningAn algorithm that recursively splits labeled data using feature tests and stopping conditions to construct a decision tree.
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  Statistics Every Programmer NeedsA decision tree is a graphical decision-analysis model that represents choices, uncertain events, possible outcomes, probabilities, and payoffs as branching paths.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA decision tree is a predictive model that recursively partitions inputs using feature-based split rules and produces outcomes at terminal leaves.
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  Ensemble Methods for Machine LearningA decision tree is a predictive model that recursively partitions data according to feature-based decisions, producing predictions at the resulting leaves.
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  Statistics Slam DunkA decision tree is a predictive model that recursively partitions data using feature-based decision rules.
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  Outlier Detection in PythonA decision tree is a hierarchical predictive model that recursively partitions observations using feature-based decision rules.
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  Grokking AI Algorithms, Second EditionA decision tree is a predictive model that recursively applies feature-based tests to partition examples and produce a classification or regression outcome.
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  Data Without LabelsA tree-structured predictive model that recursively splits data into nodes using decision rules.
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  Inside AIA decision tree is a hierarchical predictive model that recursively splits data according to attribute-based questions to reach decisions or predictions.
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  Machine Learning Algorithms in DepthA decision tree is a hierarchical predictive model that makes decisions by applying feature-based tests at internal nodes.
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  Machine Learning Platform EngineeringA decision tree is a predictive model that recursively partitions input data using feature-based decision rules.
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  Data-Driven CybersecurityA predictive model that recursively partitions data through feature-based decision rules arranged in a tree structure.

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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 Demand Forecasting Best Practices. The section links point to the parts of the book it draws on.