What is a Decision Tree?
Have you ever made a decision knowing your choice has significant consequences? If so, you know that it’s tough to determine the best approach when you’re not sure what the outcomes will be. That’s where decision trees come in. Read on to learn more about decision trees, their types, applications, advantages and disadvantages.
Understanding Decision Trees
A decision tree is a form of supervised machine learning that categorizes or makes predictions based on how you answer a previous set of queries. You can train and test it on a data set containing the desired categorization.
A decision tree represents data in a tree-like flowchart. Its primary purpose is to break an extensive dataset down into subsets that contain instances with comparable values to understand the possible outcomes of specific options.
Types of Decisions
A decision tree has two types. These include:
1. Continuous Variable
A continuous variable decision tree, also called a regression tree, is a decision tree with a continuous target variable. For instance, it can predict an individual’s income based on age, occupation and other continuous variables.
2. Categorical Variable
Categorical variable decision trees include target variables broken down into categories. For instance, the categories can be yes or no. The tree’s categories ensure that every phase of the decision process falls under one class and that there are no in-betweens.
Applications of Decision Trees
Here are some applications of decision trees:
1. Using Demographic Data To Find Potential Clients
One of the applications of a decision tree is using demographic data to find potential clients. It can help streamline a company’s marketing budget and help management make an informed decision on the company’s target market. Without decision trees, a company may spend its marketing market with no specific demographic in mind, affecting its overall revenue.
2. Recommending Related Products
A decision tree is also valuable for customer recommendation engines. A recommendation engine is an advanced filtering system that uses behavioral data, statistical modeling and computer learning to predict the product, content or services customers will like. For example, Netflix’s recommendation engine can suggest another horror movie after you’ve finished watching one.
You can structure a recommendation engine using a decision tree, taking the decisions made by customers over time and making nodes based on those decisions.
3. Serving as a Support Tool in Various Fields
Lending institutions also use decision trees to forecast the probability of a customer defaulting on a loan by using predictive model generation using the past data of the client. A decision tree support tool can help a lending institution assess an individual’s creditworthiness to prevent losses.
A decision tree also applies to strategic management and logistics planning. Other fields where a decision tree applies include healthcare, law, education and engineering.
Benefits of Decision Trees
Here are some of the benefits of decision trees:
1. Decision Trees Are Easy to Prepare
Compared with other decision techniques, a decision tree requires less data preparation. This is because you can access ready-to-use data to generate relevant variables that predict the target variable. Decision trees make classifications of data easy, as it doesn’t require you to perform complex calculations.
2. Decision Trees Are Easy to Read and Interpret
Another advantage of using a decision tree is that its output is easy to read and interpret. It doesn’t require prior knowledge of statistics. For instance, when using decision trees to present customers’ demographic information, a company’s marketing team can read and analyze the graphical representation of the data without requiring knowledge of statistics.
The data can also generate important insights into the costs, probabilities and alternatives to various strategies of the marketing department.
Drawbacks of Decision Trees
Here are some disadvantages of decision trees:
1. Decision Trees Are Less Effective in Forecasting the Outcome of a Continuous Variable
A decision tree algorithm is less effective in making forecasts when the primary goal is to predict the result of a continuous variable. It tends to lose information when breaking down variables into multiple categories.
2. Decision Trees Can Be Unstable
Another limitation of a decision tree is that it’s more unstable than other decision predictors. A slight adjustment in the data can significantly change the tree’s structure, which can convey a different result from what a user will get in a normal event. However, you can use machine learning algorithms, such as bagging and boosting, to manage the resulting change.
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A decision tree algorithm is a powerful tool for categorizing data and weighing ideas’ risks, costs and potential benefits. It allows you to make systematic, bias-free and fact-based decisions. The outputs present alternatives in an easy-to-understand format, making them useful in various environments. As a data scientist, decision trees are a key part of your tool kit.
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