Decision tree analysis
From apppm
Abstract
A common machine learning algorithm called decision tree analysis is used to categorize and predict outcomes based on a collection of input features [1]. Each node represents a choice, and each branch represents one or more potential outcomes, creating a tree-like model of decisions and their potential effects. In order to decide what actions to take at each node based on the input features, the algorithm learns from a training collection of labeled data.
Many industries, including banking, medicine, marketing, and engineering, use decision tree analysis [2]
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