Decision trees and tree ensembles are powerful learning algorithms used in many applications and also to win machine learning competitions. "it is a tool well worth having in your toolbox."[1]
"Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features."[2]
"A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules."[3]
"Decision trees in machine learning provide an effective method for making decisions because they lay out the problem and all the possible outcomes. It enables developers to analyze the possible consequences of a decision, and as an algorithm accesses more data, it can predict outcomes for future data."[4]
Decision trees are useful for breaking down complex data into manageable parts.
[1] Andrew Ng, Stanford University & DeepLearning.AI, Machine Learning Specialization, Course 2, Week 4
[2] Tree, scikit-learn.org
[3] Decision tree, Wikipedia
[4] Decision Tree, Machine Learning, Coursera