Explain the decision tree approach with suitable example

Decision Tree Approach:

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A decision tree is a visual representation of decision-making processes. It systematically breaks down a decision into a series of choices and possible outcomes. Each node in the tree represents a decision or a test on an attribute, each branch represents an outcome of that test, and each leaf node represents a final decision or a target variable. Decision trees are widely used in machine learning and data analysis for classification and regression tasks.

Example:

Consider a decision tree for predicting whether a person will play golf based on weather conditions. The target variable is “Play Golf,” and the attributes are “Outlook,” “Temperature,” “Humidity,” and “Windy.”

Decision Tree:
                      Outlook
                     /   |    \
                    /    |     \
              Sunny  Overcast  Rainy
              /        |         \
             /         |          \
        Humidity     Temperature   No
         /    \          |
        /      \         |
       High    Normal    No
        |        |
        |        |
        No       Yes

Explanation:

1. Root Node (Outlook): The first decision is based on the “Outlook” attribute, which can be “Sunny,” “Overcast,” or “Rainy.”
2. Branches: Each branch represents one of the possible values of the “Outlook.” For example, if the outlook is “Sunny,” then the tree moves to the next decision node related to humidity.
3. Decision Nodes: Subsequent decision nodes represent tests on other attributes, such as “Humidity” and “Temperature.”
4. Leaf Nodes: The leaf nodes contain the final decision. For example, if the outlook is “Sunny,” humidity is “High,” and temperature is “Normal,” the decision is “No” for playing golf.
5. Path from Root to Leaf: The path from the root to a leaf represents a series of decisions leading to a final outcome.

This decision tree can be used to make predictions. Given the weather conditions, follow the path from the root to a leaf to determine whether the person will play golf or not.

Decision trees are interpretable and easy to understand, making them valuable for decision-making in various fields, including business, finance, and healthcare. They are also the basis for more advanced ensemble methods like random forests.