Decision Trees: Definition, Explanation, and Use Cases

In the realm of Artificial Intelligence (AI), decision trees represent a significant and widely utilized machine learning algorithm. They are employed in various sectors, such as healthcare, finance, and marketing, to make predictions based on historical data. This glossary entry will delve into the intricate details of decision trees, their definition, explanation, and various use cases.

Decision trees are a type of supervised learning algorithm that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population into two or more homogeneous sets based on the most significant splitter/differentiator in input variables.

Definition of Decision Trees

At its core, a decision tree is a graphical representation of possible solutions to a decision based on certain conditions. It's called a decision tree because it starts with a single box (or root), which then branches off into a number of solutions, just like a tree. Each internal node of the tree corresponds to an attribute, and each leaf node corresponds to a decision.

The topmost decision node in a tree which corresponds to the best predictor is called the root node. Decision trees can handle both categorical and numerical data. A decision tree is built top-down from a root node and involves partitioning the data into subsets that contain instances with similar values (homogenous).

Components of a Decision Tree

A decision tree consists of three types of nodes: Decision nodes - typically depicted by squares, Chance nodes - depicted by circles, and End nodes - depicted by triangles. The nodes are connected by branches that represent the decision path.

Decision nodes, represented by squares, are used to make decisions. Chance nodes, represented by circles, show the probabilities of certain results. End nodes, represented by triangles, show the final outcome of a decision path. The branches represent the decision rules.

Explanation of Decision Trees

Decision trees use multiple algorithms to decide to split a node into two or more sub-nodes. The creation of sub-nodes increases the homogeneity of resultant sub-nodes. In other words, we can say that the purity of the node increases with respect to the target variable. The decision tree splits the nodes on all available variables and then selects the split which results in most homogeneous sub-nodes.

The algorithm selection is also based on the type of target variables. Let’s look at the most commonly used algorithms in decision tree:

ID3 (Iterative Dichotomiser 3)

ID3 algorithm uses Entropy function and Information gain as metrics. Entropy is the measure of the randomness in the information being processed. The higher the entropy, the harder it is to draw any conclusions from that information. Information gain is the decrease in entropy. Information gain computes the difference between entropy before split and average entropy after split of the dataset based on given attribute values.

ID3 iterates through every unused attribute of the set and calculates the entropy and information gain of that attribute. It then selects the attribute with the smallest entropy or largest information gain. The set is then split by the selected attribute to produce subsets of the data. The algorithm continues to recurse on each subset, considering only attributes never selected before.

C4.5 (Successor of ID3)

C4.5 is the successor to the ID3 algorithm, and it is used to generate a decision tree which will be used to classify future samples. The C4.5 algorithm has a few base cases. First, if all the samples in the list belong to the same class, it returns a leaf node with the class label. Second, if the list of samples is empty, then it returns a decision node that has a label that is the most common target value in the examples. Finally, if the attribute list is empty, it returns a decision node that has a label that is the most common target value of the samples.

In addition to handling the training data in the form of the attribute-value pairs, the C4.5 algorithm also accommodates missing attribute values, attributes with differing costs, and continuous attributes. It also provides the capability of using a default class in its decision tree. This default class is used when no previously unseen attribute-value pair is found in the decision tree.

Use Cases of Decision Trees

Decision trees have a wide range of applications in various fields. They are used in medical research and practice for diagnostic testing and in the field of genomics. In business, decision trees are used for strategy formulation, decision making, and to visualize cost effectiveness. In finance, they are used for option pricing. In machine learning and data mining, decision trees are used for classification and regression.

For example, in the healthcare sector, decision trees can be used to predict patient readmissions based on their medical history and treatment. In finance, decision trees can be used to predict the likelihood of a customer defaulting on a loan based on their credit history. In marketing, decision trees can be used to predict customer churn based on their purchase history and engagement with marketing campaigns.

Decision Trees in Healthcare

In the healthcare sector, decision trees have been used to develop predictive models for a wide range of outcomes, from predicting patient readmissions to identifying risk factors for serious diseases. For example, a decision tree could be used to predict whether a patient with a specific set of symptoms is likely to have a certain disease. This can help doctors to make more accurate diagnoses and provide more effective treatments.

Another use of decision trees in healthcare is in the field of genomics, where they are used to identify genetic markers for diseases. By analyzing the genomes of individuals with and without a particular disease, researchers can use decision trees to identify genetic variations that are associated with the disease. This can help to identify individuals at risk of developing the disease and could also lead to the development of new treatments.

Decision Trees in Finance

In the finance sector, decision trees are used in a variety of ways, from predicting stock market trends to assessing the risk of lending to a particular individual or company. For example, a bank might use a decision tree to predict whether a potential borrower is likely to default on a loan. The decision tree might consider factors such as the borrower's credit score, income, employment status, and other financial information.

Another use of decision trees in finance is in the field of option pricing. An option is a financial derivative that gives the holder the right, but not the obligation, to buy or sell an underlying asset at a specified price within a certain time period. Decision trees can be used to model the potential future price movements of the underlying asset, which can help to determine the fair price of the option.

Decision Trees in Marketing

In the field of marketing, decision trees are used to predict customer behavior and to develop targeted marketing campaigns. For example, a company might use a decision tree to predict which customers are most likely to churn, or stop doing business with the company. The decision tree might consider factors such as the customer's purchase history, engagement with marketing campaigns, and demographic information.

Another use of decision trees in marketing is in the field of customer segmentation. By analyzing customer data, companies can use decision trees to identify distinct groups of customers with similar characteristics. This can help companies to develop targeted marketing campaigns that are tailored to the needs and preferences of each customer segment.

Conclusion

Decision trees are a powerful tool in the field of artificial intelligence, with wide-ranging applications in sectors such as healthcare, finance, and marketing. They offer a visual and intuitive way to make predictions based on historical data, making them a popular choice for both classification and regression tasks.

Despite their simplicity, decision trees can be used to solve complex problems and can handle both categorical and numerical data. Their ability to handle missing data, attributes with differing costs, and continuous attributes make them a versatile tool in the field of machine learning. Whether you're predicting patient readmissions, assessing credit risk, or predicting customer churn, decision trees offer a robust and interpretable solution.