What Is a Decision Tree
A decision tree is a tree-shaped diagram that shows statistical probability or determines a course of action. It shows the steps to take and why one choice may lead to another. Therefore, it is a suitable decision-making tool for research analysis or for planning the strategy to reach a goal.
A decision tree has three main parts: a root node, leaf nodes, and branches. The root node is the target value that we are seeking to reach. The leaf nodes contain the information about criteria. The branches connect the nodes and show the route through the leaves to the target value.
Making the Tree
At first sight, it may look difficult to create a decision tree from raw data. The good news is that AnswerMiner builds the tree automatically from the dataset. There are only a few steps to take to make a decision tree:
- Select the target value or starting point, which will be the root node.
- Highlight the columns that you would like to involve.
- The branches will be created automatically and show the strength of the connections between the nodes.
Watch the short video below to see how it works:
Tree in Action
More and more sectors are using decision trees because they are a powerful visualization tool. They are most commonly used in the financial sector by loan approval or portfolio management. In our data-driven world, other sectors have also discovered their usefulness and apply them to optimize their strategies to reach their goals and avoid pitfalls.
The decision tree is a hidden weapon to make a prediction using your data. The main question is how much time do you need to analyze and understand your data? AnswerMiner makes the exploration of data much faster and easier and makes decision trees in a second.