In other words, we can say that a decision tree is a hierarchical tree structure that can be used to split an extensive collection of records into smaller sets of the class by implementing a sequence of simple decision rules. It helps us to make the best decisions based on existing data and best speculations. It provides us a framework to measure the values of outcomes and the probability of accomplishing them. It enables us to analyze the possible consequences of a decision thoroughly. Starting with the dataset, we can measure the entropy to find a way to segment the set until the data belongs to the same class. In short, a decision tree is just like a flow chart diagram with the terminal nodes showing decisions. Building a decision tree is all about discovering attributes that return the highest data gain. Information Gain refers to the decline in entropy after the dataset is split. In the decision tree, it measures the randomness or impurity in data sets. Key factors: Entropy:Įntropy refers to a common way to measure impurity. Decision trees can deal with both categorical and numerical data. We can't accomplish more split on leaf nodes-The uppermost decision node in a tree that relates to the best predictor called the root node. The leaf nodes show a classification or decision. A decision node has at least two branches.
The final tree is a tree with the decision nodes and leaf nodes. It separates a data set into smaller subsets, and at the same time, the decision tree is steadily developed. The decision tree creates classification or regression models as a tree structure. It is a tree that helps us in decision-making purposes. Decision Tree is a supervised learning method used in data mining for classification and regression methods.