Decision Trees for Decision-Making (2024)

FAQs

How can a decision tree be used in decision making? ›

Summary. Decision tree analysis involves visually outlining the potential outcomes, costs, and consequences of a complex decision. These trees are particularly helpful for analyzing quantitative data and making a decision based on numbers.

Another common challenge of decision trees is dealing with tree complexity, which refers to how large and deep your tree is. A complex tree can have many branches, nodes, and splits, which can make it difficult to understand and explain.

Decision trees are extremely useful for data analytics and machine learning because they break down complex data into more manageable parts. They're often used in these fields for prediction analysis, data classification, and regression.

One of the primary disadvantages of decision trees is their ability to overfit the training data. Overfitting occurs when the tree is too deep and complex, capturing noise in the data rather than the underlying patterns. This leads to poor generalization to new, unseen data.

Decision trees are commonly used in business for analyzing customer data and making marketing decisions, but they can also be used in fields such as medicine, finance, and machine learning. The most detailed decision trees can be incredibly complex, but simple decision trees are easy to create and interpret.

Prone to Overfitting

CART Decision Trees are prone to overfit on the training data, if their growth is not restricted in some way. Typically this problem is handled by pruning the tree, which in effect regularises the model.

They are not well-suited to continuous variables (i.e. variables which can have more than one value, or a spectrum of values). In predictive analysis, calculations can quickly grow cumbersome, especially when a decision path includes many chance variables.

Decision trees are commonly used in operations research and operations management. If, in practice, decisions have to be taken online with no recall under incomplete knowledge, a decision tree should be paralleled by a probability model as a best choice model or online selection model algorithm.

The probability of all outcomes must add up to 1. The Expected Value (EV) shows the weighted average of a given choice; to calculate this multiply the probability of each given outcome by its expected value and add them together eg EV Launch new product = [0.4 x 30] + [0.6 x -8] = 12 - 4.8 = £7.2m.

What is a decision tree in simple terms? ›

A decision tree is a tree-like structure that represents a series of decisions and their possible consequences. It is used in machine learning for classification and regression tasks. An example of a decision tree is a flowchart that helps a person decide what to wear based on the weather conditions.

Real-World Example

Let's assume that a clothing store is opening a second location and wants to decide whether to open in San Francisco or New York. Opening a location in either city will involve different capital expenditures and demonstrate different rates of success.

The decision tree is a fairly simple algorithm that works by creating rules based on the features in our dataset. As we add more features, the complexity of the decision tree increases, which means it will take longer to train, and it's also more likely to overfit our training data.

Decision-tree methods are biased towards (hyper) rectangular patterning. We tend to see this with discrete data, and whenever ranges or thresholds are significant in numeric data. Addressing a bias-mismatch by increasing representational turns the model into a `lookup table'.

Accuracy can be computed by comparing actual test set values and predicted values. We got a classification rate of 67.53%, which is considered as good accuracy. You can improve this accuracy by tuning the parameters in the decision tree algorithm.

Here is a [recently developed] tool for analyzing the choices, risks, objectives, monetary gains, and information needs involved in complex management decisions, like plant investment.

The Decision trees are considered an efficient method to make decisions or solve problems under uncertainty to evaluate each choice based on the outcome and compare options based on these expected outcomes. Related problem-solving articles: Problem-Solving Using Cause and Effect Diagram.

Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning.