Decision Trees and Decision Tables

System Processing Tools Below: Decision Trees and Decision Tables

• Decision Trees and Decision Tables are two very important System Processing Tools.

Decision Tree

Introduction

• Decision trees are a powerful tool for both classification and regression tasks, providing a clear and interpretable model.
• By understanding the structure and functioning of decision trees, one can effectively apply them to various decision-making and predictive modeling tasks.

Definition

• A decision tree is a graphical representation used to make decisions and predict outcomes by mapping various possible choices and their possible consequences.

Components of a Decision Tree

The following major components are used in making a decision tree:-

• Root Node:
  • The topmost node in a decision tree represents the entire dataset or the initial question that needs to be answered.
• Decision Nodes:
  • These internal nodes represent decision points or tests on an attribute.
• Branches:
  • This is the connectors between nodes that represent the outcome of a decision or test.
  • Example: “Yes” or “No” based outcomes.
• Leaf Nodes (Terminal Nodes):
  • Endpoints of a decision tree that represent the outcome or decision.

Working Mechanism

• Decision trees operate by recursively splitting the data into subsets based on the value of input attributes.
• This process continues until all data is classified or a stopping criterion is met.
• The tree structure allows easy interpretation of the decision-making process.

Types of Decision Trees

• Classification Trees:

  • This decision tree is used when the output is a categorical variable.
  • The tree classifies the input data into predefined classes.
  • Example: Determining whether an email is “Spam” or “Not Spam”.

• Regression Trees:

  • This decision tree is used when the output is a continuous variable.
  • The tree predicts the value of the output variable based on the input variables.
  • Example: Predicting the price of a house based on its features.

Advantages of Decision Trees

• Decision trees are intuitive, simple to understand & interpret, and easy to visualize, making them accessible to non-experts.
• Decision trees do not require the normalization of data, making them straightforward to apply.
• Decision trees can handle different types of input data, making them versatile.

Disadvantages of Decision Trees

• Decision trees can easily overfit the training data, capturing noise instead of the underlying pattern.
• Small changes in the data can result in significantly different trees, making them less stable.
• If some classes dominate, the tree might become biased towards the dominant class.

Tools and Libraries for Decision Trees

• Scikit-learn: It is a popular machine-learning library in Python that provides implementations of decision trees.
• R: It offers packages like rpart and party for building decision trees.
• Weka: It is a data mining software that includes decision tree algorithms.

Example of a Decision Tree

(i) A decision tree for deciding whether to play outside based on weather conditions:

Root Node: Is it sunny?

• • Decision Node: Yes
    • Decision Node: Is it hot?
      • Leaf Node: Yes -> Stay inside
      • Leaf Node: No -> Play outside
  • Decision Node: No
    • Leaf Node: Stay inside

(ii) A graphical representation/example of the decision tree are – 

Use/Applications

• It is widely used in data mining, machine learning, and decision analysis. 

Decision Table

Introduction

• Decision tables are an effective tool for modeling complex decision-making scenarios in a structured and comprehensive way.

Definition

• A Decision Table is a tabular method for representing and analyzing decision rules and provides a systematic way to identify and document various conditions and the corresponding actions to take based on those conditions. 
• A decision table shows the way the system handles input conditions and subsequent actions on the event.

Characteristics

• They enhance clarity, ensure consistency, and facilitate effective documentation and testing of decision rules.
• Decision tables are particularly useful in complex decision-making processes where multiple conditions and actions need to be considered simultaneously.

Components of a Decision Table

• A decision table is composed of rows and columns, separated into four separate quadrants.

•  There are the following components used or focussed during the creation of a Decision Table –
• • Conditions:
    • These are the different scenarios or criteria that need to be evaluated.
  • Actions:
    • These are the possible outcomes or decisions that will be executed based on the conditions.
  • Condition Entries:
    • These are the possible states or values for each condition.
  • Action Entries:
    • These are the specific actions to be taken for each set of conditions.

Structure of a Decision Table

A typical decision table consists of four quadrants:-

• Conditions Stub: It lists/includes all the conditions.
• Actions Stub: It lists/includes all the possible actions.
• Conditions Entries: It specifies the values for each condition in different scenarios.
• Actions Entries: It indicates the actions to be taken for each combination of condition values.

Example of a Decision Table

A simplified example of a decision table for an online store’s order processing system is:-

In this table:

• Customer Type and Order Amount > 500 are the conditions.
• Offer Discounts, Free Shipping, and Priority Handling are the actions.
• The rules specify different combinations of conditions and the corresponding actions.

Benefits of Decision Tables

• They provide a clear and concise way to represent complex decision logic.
• It helps to ensure that all possible combinations of conditions are considered, reducing the likelihood of missing a condition.
• It is a formal documentation of business rules, which is useful for auditing and compliance.
• It facilitates the creation of test cases for software testing by clearly defining the input conditions and expected outputs.

Working Steps to Create a Decision Table

• Identify Conditions: First of all list all the conditions that affect the decision-making process.
• Identify Actions: Now, list all the possible actions that can be taken.
• Define Condition Entries: Now, determine the possible values or states for each condition.
• Define Action Entries: Now, specify the actions to be taken for each combination of condition values.
• Review and Simplify: Check for any redundant rules or conditions and simplify the table if possible.

Use/Applications of Decision Tables

• Business Rules Modeling: It helps in capturing complex business rules in the insurance, finance, and healthcare sectors.
• Software Engineering: It is used to specify logic for system behavior, especially in rule-based systems.
• Testing and QA: It creates comprehensive test cases to ensure all scenarios are covered. 

System Processing Tools above: Decision Trees and Decision Tables.