Decision Tree Learning in Machine Learning

 What is a decision tree?

A decision tree is a tree-like structure where each internal node represents a feature (attribute) of the data, and each branch represents a decision rule based on that feature. The leaves of the tree represent the predicted outcome or class label.

Here's an example of a decision tree for predicting whether someone will buy a car:

 

In this example, the root node is "Income." If a person's income is high, they are more likely to buy a car, so the tree branches to "Credit score." If their credit score is good, they are very likely to buy a car, so the tree ends at a leaf node labeled "Yes." If their credit score is bad, they are less likely to buy a car, so the tree branches to "Age." If they are young, they are still somewhat likely to buy a car, so the tree ends at a leaf node labeled "Maybe." If they are old, they are not very likely to buy a car, so the tree ends at a leaf node labeled "No."

How does decision tree learning work?

Decision tree learning algorithms work by iteratively splitting the data into smaller subsets based on the values of the features. The algorithm chooses the feature that best separates the data into groups with different target values. This process continues until the data is sufficiently separated, or until a certain stopping criterion is met.

There are many different decision tree learning algorithms, but they all follow the same basic principles. Some popular decision tree learning algorithms include CART, C4.5, and ID3.

The following is the simple root map for Decision Tree Learning

1. Appropriate problems for Decision Tree Learning

2. The basic Decision Tree Learning Algorithm 

        2.1. How to calculate Entropy and Information Gain

         2.2. Example-1: To Buy a Computer Dataset

         2.3. Example-2: Play Tenses Dataset  

         2.4. Example-3: patients visitation dataset

3. Pruning in Decision Tree

4. Rules extraction from Decision Tree 

5. Learning rules from data

6. Issues in Decision Tree Learning

7. Inductive bias in Decision Tree  

8. Advantages and disadvantages of Decision Tree Learning