About Decision Tree

Decision Tree classifies data using a tree structure of rules, making predictions clear and easy to interpret.

Decision tree is a supervised machine learning algorithm used for classifying data. Decision tree has a tree structure built top-down that has a root node, branches, and leaf nodes. In some applications of machine learning, the reason for predicting one outcome or another may not be important in evaluating the overall quality of a model. In others, the ability to explain the reason for a decision can be crucial. You can use decision tree rules to validate models in such problems. The Decision Tree algorithm, like Naive Bayes, is based on conditional probabilities. Unlike Naive Bayes, decision trees generate rules. A rule is a conditional statement that can be understood by humans and used within a database to identify a set of records.

For example, a Marketing professional requires complete descriptions of customer segments to launch a successful marketing campaign. The Decision Tree algorithm is ideal for this type of application.

Use decision tree rules to validate models. If the rules make sense to a subject matter expert, then this validates the model.