components

CS 422: Data Mining

• Instructor: Vijay K. Gurbani, Ph.D.

• Institution: Illinois Institute of Technology

• Lecture: 4

• Topic: Components of Learning, Decision Trees

Decision Trees Overview

• Decision trees offer a structured way to represent decisions and their possible consequences.

• Structure of Decision Tree Example:

  • Root Node (Decision Point)

  • Internal Nodes (Attributes)

  • Leaf Nodes (Outcomes or Classes)

Example Data Representation

• Example Records:

  • Tid, Attrib1, Attrib2, Attrib3, Class

  • Various attributes such as ownership, size, income are used to categorize data.

  • Class indicates the outcome based on attribute values (e.g., Yes/No).

Induction Process

• Learning from Data:

  • Training set used to create a model.

  • The model applies learned rules to make deductions on new data (Test Set).

Hunt's Algorithm

• Key algorithm for constructing decision trees.

• General Procedure:

  • Let Dt be the training records at node t.

  • If Dt contains records of the same class, then:

    • t becomes a leaf node labeled yt.

  • If Dt is empty, t is labeled by the default class.

  • If Dt contains multiple classes, perform the following:

    • Use an attribute test to split Dt into subsets by chosen attribute.

  • Recursively apply the same procedure to each subset.

Detailed Steps of Hunt's Algorithm

1. Identification of Leaf Nodes:

   • Simpler conditions where all records belong to a single class.

2. Handling Empty Sets:

   • Use default class labeling when no records are available.

3. Attribute Testing:

   • Benefits recursive application to further subdivide records for more granularity.

Default Class Considerations

• The default class serves as a fallback when there is insufficient information to make a decision.

• Different default classes can be assigned based on the attributes involved, influencing the final decision.

Decision Tree Attributes

• Examples of Attributes:

  • Homeowner status

  • Marital status

  • Annual income level

• A specific structure can emerge based on attribute values leading to certain classifications.

Implementation and Code References

• Mentioned resources include:

  • loan.r: Script for constructing decision tree.

  • loan.csv: Dataset used in implementing decision tree scenarios.

Summary of Key Concepts

• Decision trees are versatile for classification tasks in data mining.

• Understanding Hunt's algorithm is crucial for creating effective decision trees.

• Decision trees follow a clear structure with well-defined leaf nodes and decision paths.