309 results for "decision tree"

A decision tree describes a flowchart or algorithm that analyzes the pathway toward making a decision. The basic flow of a decision based on data starts at a single node and moves through branches into two or more directions, giving the appearance of a tree, so to speak. Each branch offers a different possible outcome, depending on the decisions made and random events that occur. A decision tree is often used in data science and data analytics, but can also be used in evaluating decision-making processes in other areas of a company, like human resources, operations, and inventory management.‎

Decision trees are important because they serve to make visual these complex data parts into manageable pieces of information. Humans can better understand what decisions need to be made when they flow through a decision tree. An example of a decision tree in visual form might show where each level needs to have a decision made for it. As each of the answers are chosen, this visual pathway will eventually help you to come to a decision about the initial problem. This is why decision trees are important. When used in areas like machine learning, data regression, prediction analysis, and data classification, decision trees can ramp up the capacity to tackle complicated decision tasks.‎

Typical jobs that use decision trees include data integration developer, data scientist, decision tree analysis expert, automation development manager, and similar roles that seek to organize and manage data flows. These jobs are often aligned with software programming jobs, so learning about programming can help to better understand the data analysis needed in decision tree work. Decision trees are also used in other work areas, like operations and HR, so learning decision trees may be able to help you work in these functions as well.‎

When you take an online course to learn about decision trees, you might learn to think more analytically about the decision-making process and the steps involved in looking at different angles of a problem. You may also learn about the basic steps to visualizing a problem, and how to use sets of data to do that. Taking online courses to learn decision trees may also prepare you for eventual interest in machine learning models using top industry tools.‎