Decision trees

A decision tree, in the traditional sense and in artificial intelligence (AI), is a type of flowchart that visualizes potential outcomes through simple decisions. In both traditional and AI cases, decision trees are used to assess and analyze key information before important decisions are made. Decision trees have been used in many fields.

Discover the significance of decision trees in AI tools with Micron and explore their key applications.

What is a decision tree?

Decision trees definition: Decision trees are a type of ​machine learning model that supervise learning algorithms, which can be used for regression tasks.  

As suggested in the name, decision trees have a hierarchical structure, consisting of different “branches.” Each branch represents a different decision, leading to another branch or decision. 

The tree is usually shown upside down, with roots at the top that feed into each other at the next level down in the decision tree. The model begins with the root node, which then feeds into the internal nodes, which in turn lead into the leaf nodes. 

These nodes all have different purposes. The root node is the beginning of the model. The internal nodes are where data is split into subsets, which then get fed into the leaf nodes. These make up the end of the model, where results of the analyzed data are presented. 

How do decision trees work?

Decision trees process input data by running through a series of categorization questions, each yielding binary answers. Each binary answer results in another question, creating a branching flow toward a range of output results.

The process can be repeated until all possible output results are generated. The strength of this AI model is that it yields a wide range of results and potential answers rather than one result. This approach allows data scientists to either reanalyze the results through another decision tree or retain some manual control over the final selection if a single result is required. 

What is the history of decision trees?

Computer decision trees are a type of machine learning model that has been in development for many decades. 

• 1960s, initial design: The Iterative Dichotomiser 3 (ID3) is an algorithm first developed in the 1960s, pioneering the use of decision trees in creating artificial intelligence. 
• 1970s, CART algorithm design: Not long after the ID3 algorithm was introduced, a group of researchers (Breiman, Stone, Friedman and Olshen) developed the classification and regression tree model, known as CART. 
• 1990s, random forest evolution: In the early 1990s, decision trees evolved into ​random forests, which combined multiple decision trees to produce more complex analytical processes and more accurate results. 

What are the key types of decision trees?

All types of AI decision tree algorithms are based on a shared foundation. These three aspects are part of this foundation and can be used with machine learning and ​AI-powered tools: 

The Iterative Dichotomiser 3 algorithm helps evaluate candidate splits by using information gain and entropy to initiate the analysis process. 

• C4.5 is considered an advanced iteration of the ID3 algorithm. It evaluates natural split points within the decision tree by analyzing information gained. This approach makes it more effective in identifying the most informative attributes for classification. 

The CART algorithm finds the best place within the decision tree to split by using Gini impurity (the probability that a randomly chosen element from the dataset will be misclassified). It can be used for classification- and regression-based tasks. 

Classification and regression tree (CART)

An abbreviation of classification and regression trees, this algorithm finds the best place within the decision tree to split on by using ‘Gini impurity.’ It can be used for both classification and regression-based tasks.

How are decision trees used?

Decision trees are a simple concept, but they can be widely applied across all industries. Like many predictive and analytical tools using artificial intelligence, certain fields significantly benefit from implementing decision trees and machine learning. Any sector where decisions are made based on complex datasets can effectively use decision trees to improve efficiency and accuracy. Marketing, finance, and healthcare are prime examples of industries that use decision trees to enhance their decision-making processes. 

A key reason for using decision trees in marketing is to tailor marketing campaigns to subsections of a particular business' clientele. Decision trees give companies the ability to dissect and analyze customer data and then use that information to create subcategories of customers who behave similarly. This approach can guide a business’s strategy for marketing to these customer subsets more effectively. 

Decision trees also allow companies to predict these customers' patterns and behaviors. Using this model, companies gain a better understanding of customers’ thought processes and identify reasons for their potential interest or disinterest in a brand or product. The company can then decide how best to retain these types of customers. 

Decision trees have also revolutionized the healthcare industry. These models can predict the likelihood of a patient's readmission to the hospital based on factors such as age, gender, and other general characteristics. They also help identify potential risk factors for individual patients. 

By analyzing the genes of individuals with and without particular diseases, decision trees enable healthcare professionals to identify variations that may cause these diseases in the first place. This application enhances the industry’s ability to diagnose, treat and prevent various health conditions.