## Decision Tree InductionDecision Tree is a supervised learning method used in data mining for classification and regression methods. It is a tree that helps us in decision-making purposes. The decision tree creates classification or regression models as a tree structure. It separates a data set into smaller subsets, and at the same time, the decision tree is steadily developed. The final tree is a tree with the decision nodes and leaf nodes. A decision node has at least two branches. The leaf nodes show a classification or decision. 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. Decision trees can deal with both categorical and numerical data. ## Key factors:## Entropy:Entropy refers to a common way to measure impurity. In the decision tree, it measures the randomness or impurity in data sets. ## Information Gain:Information Gain refers to the decline in entropy after the dataset is split. It is also called In short, a decision tree is just like a flow chart diagram with the terminal nodes showing decisions. 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. ## Why are decision trees useful?It enables us to analyze the possible consequences of a decision thoroughly. It provides us a framework to measure the values of outcomes and the probability of accomplishing them. It helps us to make the best decisions based on existing data and best speculations.
Consider the given example of a factory where Expanding factor costs $3 million, the probability of a good economy is 0.6 (60%), which leads to $8 million profit, and the probability of a bad economy is 0.4 (40%), which leads to $6 million profit. Not expanding factor with 0$ cost, the probability of a good economy is 0.6(60%), which leads to $4 million profit, and the probability of a bad economy is 0.4, which leads to $2 million profit. The management teams need to take a data-driven decision to expand or not based on the given data. Net Expand = ( 0.6 *8 + 0.4*6 ) - 3 = $4.2M ## Decision tree Algorithm:The decision tree algorithm may appear long, but it is quite simply the basis algorithm techniques is as follows: The Generally, we refer to Initially, The parameter
## Advantages of using decision trees:A decision tree does not need scaling of information. Missing values in data also do not influence the process of building a choice tree to any considerable extent. A decision tree model is automatic and simple to explain to the technical team as well as stakeholders. Compared to other algorithms, decision trees need less exertion for data preparation during pre-processing. A decision tree does not require a standardization of data. Next TopicEducational Data Mining |