Decision tree

As explained in Data Without Labels

A tree-structured predictive model that recursively splits data into nodes using decision rules.

A decision tree is presented as one algorithm for regression problems, alongside linear regression, random forest, k-nearest neighbor, boosting, and neural networks. section 1.5.1

Its structure begins with a root node; splitting produces decision nodes and terminal nodes, with terminal nodes marking points that cannot be split further. section 1.5.1

Figure 1.13 A decision tree has a root node, and after splitting, we get a decision node and a terminal node, which is the final node and cannot be split further.

Inspect the diagram of a root node splitting into a decision node and a terminal node. section 1.5.1

How Data Without Labels covers it

How deep it goes

Defines roots, decision nodes, and terminal nodes, but does not explain split criteria, training, or prediction procedures.

Examples

Places decision trees beside a height-to-weight regression problem and a structured telecom churn dataset.

What it assumes

Assumes basic supervised-learning concepts such as independent variables, target variables, labels, and regression.

What you can do after

Evaluate/understand a decision tree’s basic node-and-split structure and recognize it as a regression option.

How it connects

Connects decision trees with supervised learning, regression, random forests, k-nearest neighbors, boosting, and neural networks.

liveBookmanning.com

Read Data Without Labels on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Grokking Machine LearningAn algorithm that recursively splits labeled data using feature tests and stopping conditions to construct a decision tree.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  Statistics Slam DunkA decision tree is a predictive model that recursively partitions data using feature-based decision rules.
• 

  liveBookmanning.com
  Outlier Detection in PythonA decision tree is a hierarchical predictive model that recursively partitions observations using feature-based decision rules.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  Inside AIA decision tree is a hierarchical predictive model that recursively splits data according to attribute-based questions to reach decisions or predictions.
• 

  liveBookmanning.com
  Machine Learning Algorithms in DepthA decision tree is a hierarchical predictive model that makes decisions by applying feature-based tests at internal nodes.
• 

  liveBookmanning.com
  Machine Learning Platform EngineeringA decision tree is a predictive model that recursively partitions input data using feature-based decision rules.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  Data-Driven CybersecurityA predictive model that recursively partitions data through feature-based decision rules arranged in a tree structure.

Compare how 13 books explain decision tree →

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 Data Without Labels. The section links point to the parts of the book it draws on.