Decision Tree

What is a Decision Tree?

A decision tree is a graphical representation of the various alternatives available to solve a given problem to determine the most effective course of action. Decision trees consist of nodes and branches - nodes represent a test on an attribute, and branches represent possible outcomes.

What is Decision Tree Analysis?

Decision trees are tree-based models used to support decision-making by visualizing outcomes, consequences, and costs. You can quickly evaluate and compare the "branches" to determine which course of action is best for you. Making complex decisions about cost management, operations management, organization strategies, project selection, and production methods is easier with decision tree analysis.

How does a Decision Tree work?

The decision tree diagram is drawn from left to right and is composed of "burst" nodes split into different paths. There are three types of nodes: Root nodes, which compile the entire sample and are then divided into multiple sets; Decision nodes, which are typically represented by squares, represent sub-nodes that diverge further into further possibilities; and Terminal nodes, which represent the outcome that cannot be categorized further.

Branches or lines represent the various options, and nodes can be pruned to eliminate sub-nodes. Decision trees can be hand-drawn or created with decision tree software. Analyses can be done manually in R or using automated software.

What are the five steps of Decision Analysis?

A decision tree analysis consists of the following steps:

1. Identify the problem area that requires decision-making. 
2. Create a decision tree with all possible solutions and their consequences.
3. Include relevant variables and their probabilities.
4. Calculate and assign rewards based on what might happen.
5. Determine the most valuable solution for each chance node by calculating their Expected Monetary Value.

What are the types of Decision Trees?

The two main decision trees are categorical and continuous, based on the target variable.

1. Categorical variable decision tree

Categorical variables are divided into categories in a categorical variable decision tree. According to the categories, every stage of the decision-making process falls into one category, and there is no in-between. For instance, the categories include yes and no.

2. Continuous variable decision tree

Continuous variable decision trees are decision trees with constant targets. A person's income can be predicted, for instance, by using their occupation, age, and other continuous variables.

How can Decision Tree be applied?

Below are how Decision Tree can be applied:

1. Assessing prospective growth opportunities

Decision trees are often used for evaluating prospective business growth opportunities based on historical data. With historical sales data, an organization can develop a decision tree that helps it make changes to its strategy to aid expansion and growth.

2. Using demographic data to find prospective clients

Decision trees are also used to find prospective customers using demographic data. Businesses can use this information to streamline marketing budgets and make informed decisions about target markets. In the absence of decision trees, the business may spend its marketing market without a specific demographic in mind, which will affect its overall revenues.

3. Serving as a support tool in several fields

By applying predictive modeling to the client's past data, lenders can also calculate the probability of a customer defaulting on a loan. Lenders can assess a customer's creditworthiness using a decision tree support tool and prevent losses.

The use of decision trees in operations research can also be applied to logistics planning and strategic management, as companies can use them to determine appropriate strategies that will help them achieve their intended goals. Other fields where decision trees can be applied include engineering, education, law, business, healthcare, and finance.

How can Decision Tree be applied?

Below are how Decision Tree can be applied:

1. Assessing prospective growth opportunities

Decision trees are often used for evaluating prospective business growth opportunities based on historical data. With historical sales data, an organization can develop a decision tree that helps it make changes to its strategy to aid expansion and growth.

2. Using demographic data to find prospective clients

Decision trees are also used to find prospective customers using demographic data. Businesses can use this information to streamline marketing budgets and make informed decisions about target markets. In the absence of decision trees, the business may spend its marketing market without a specific demographic in mind, which will affect its overall revenues.

3. Serving as a support tool in several fields

By applying predictive modeling to the client's past data, lenders can also calculate the probability of a customer defaulting on a loan. Lenders can assess a customer's creditworthiness using a decision tree support tool and prevent losses.

The use of decision trees in operations research can also be applied to logistics planning and strategic management, as companies can use them to determine appropriate strategies that will help them achieve their intended goals. Other fields where decision trees can be applied include engineering, education, law, business, healthcare, and finance.

What are the advantages of a Decision Tree?

Here are three advantages of a Decision Tree:

1. Easy to read and interpret

A decision tree's output can be read and interpreted without requiring statistical knowledge, which is an advantage. Using decision trees, for example, allows marketing department staff to read and interpret graphical representations of the data without requiring prior knowledge of statistics.

Data can also provide insight into probabilities, costs, and alternatives to different marketing strategies formulated by the marketing department.

2. Easy to prepare

In comparison with other decision techniques, decision trees require less data preparation because a user must have access to ready-to-use data to create relevant variables that can predict the target variable. Creating classifications of data can also be done without complex calculations.

3. Less data cleaning required

The decision tree also has the advantage of requiring less data cleaning once the variables have been created and outliers and missing values have less significance in the data of the decision tree