If you’re looking to understand the fundamentals of decision tree in artificial intelligence, then you’ve come to the right place. Decision trees are an essential tool that play a central role in AI and machine learning. AI based decision trees can help analyse data and make decisions for us in a much more efficient way, taking into account complex criteria.
A decision tree is a type of chart that visually represents a set of decisions that need to be made based on certain criteria. It is segmented into “branches” and “nodes,” which represent possible outcomes or decisions. As data is analysed, the branches and nodes are illustrated based on the results from each analysis step. The final result is usually represented as a tree structure with two types of nodes: Classification & Regression Trees (CARTs) and Leaves/Terminal Nodes.
CARTs are used to sort data into categories based on key characteristics, while Leaves/Terminal Nodes contain either a predicted value or the actual value depending on if it’s supervised or unsupervised learning. In order to create these trees, various algorithms such as ID3, C4.5, C5.0, Random Forests, Gini Impurity Indexes and Entropy measurements are applied. When creating decision trees through AI based algorithms, one must consider the split criteria (e.g., maximum gain/gain ratio), complexity cost functions (entropy/Gini) used to calculate how well each branch fits with its associated node, and pruning methods used to reduce noise from overly complex trees without affecting its accuracy too much (i.e., pruning).
Source: What is Decision Tree in Artificial Intelligence
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