Decision trees are a popular tool in machine learning used to quickly identify and interpret complex rules from a dataset. It’s easy to see why they have become so popular as they are both accessible and powerful. But how exactly do you get started with decision trees, and why should you use them? We’ve got you covered with a comprehensive guide to decision trees, full of real-life examples and tips for success.
What Are Decision Trees?
Decision trees are a popular machine learning technique that can be used to determine outcomes or classify data. They are built from decision nodes and are split into branches based on specific criteria or conditions. Each branch represents a possible outcome and leads to a decision at the end of its path. This method makes it easy to understand and interpret the data, and intuitive visualization makes it easy to explain the results to non-technical stakeholders.
How Do Decision Trees Work?
To understand how decision trees work, it’s important to know each element’s purpose. Each decision node represents a question about the data that guides the tree towards the eventual outcome. The internal or decider node is where the decision is made about the best division of the dataset to proceed with while each branch can show either an acceptable outcome or another question.
Node Importance
Given that a node can be a decision node, a test node, or a leaf node, each plays an essential role in the efficiency and effectiveness of a decision tree. The test node and the decision node have higher significance compared to a leaf node. The decision node determines which path the data may follow, whereas the test node splits the paths at each branching point. Contrarily, the leaf node is the endpoint of a decision tree.
Why Are Decision Trees Beneficial?
Decision trees are popular for both beginners and experienced data analysts because they provide a straightforward and easy-to-interpret machine learning technique. Decision trees also help automate decision-making processes and have the benefit of focusing on relevant and weighted factors that impact the decision. The visualization tools make it easy to communicate with non-technical stakeholders to understand the rationale behind the decision.
Real-Life Example: Loan Approval
Imagine that you need a loan to buy a new car. You fill out a loan application, but the bank has to decide whether to approve your request or deny your application. Building a decision tree from historical data can help the bank assess the likelihood of you getting approval.
For example, an experienced data analyst could use parameters such as credit score, salary, and employment history to create a decision tree. The decision tree might have a few decision nodes, including:
1. Is the applicant’s credit score greater than 700?
2. Has the applicant had stable employment history for the past year?
3. Does the applicant’s monthly income exceed $4,000?
If the applicant meets all three requirements, the branch leads to approval; otherwise, the branch leads to denial. The decision tree becomes more complex if a situation requires a deep analysis of many other parameters.
Building A Decision Tree
Decision trees can be constructed using several algorithms. As a rule of thumb, there are three essential steps to building a decision tree:
1. Data collection and cleaning
2. Selecting the algorithm and relevant parameters
3. Evaluating the performance
Deciding on the algorithm, determining relevant parameters and evaluating the performance is critical when developing the tree. Factors like overfitting, underfitting and pruning are ways to modify the construction to avoid excessive complexity that could harm the model’s effectiveness or cause accuracy issues.
Real-Life Example: Predicting Audit Results
Suppose a business wants to predict the likelihood of an audit as they run tax reports for the year. They collect data about profits, size, and a company’s industry, ideal parameters to consider in predicting an audit. After evaluating the parameters, they build and test a decision tree to identify potential red flags. The decision tree may include features like:
1. Is the business out of the median range when comparing industry size?
2. Does the company’s profit match expectations based on industry averages?
3. Does the data show any unexpected tax write-offs?
Answering these questions can help the business determine the potential risk for an audit and create a data-driven approach to mitigating that risk.
The Bottom Line
Decision trees are a helpful machine learning technique that both beginners and experienced analysts can use to simplify the decision-making process. They allow you to provide a rationale for decision-making with a clear visualization tool that non-technical stakeholders can understand easily. With the right data and an appropriate algorithm, decision trees can offer deep insight into critical areas of decision-making.