# Difference between Decision Table and Decision Tree

• Difficulty Level : Basic
• Last Updated : 23 Jun, 2022

1. Decision Table: Decision Table is just a tabular representation of all conditions and actions. Decision Trees are always used whenever the processing logic is very complicated and involves multiple conditions. The main components used for the formation of the Data Table are Conditions Stubs, Action Stubs, and rules.

Types of decision tables:

• Extended entry table
• Limited entry table

Benefits:

• Visualization of Cause and effect relationships in the table.
• Easy to understand
• In the case of a complex table, it can be readily broken down into simpler tables.
• Tables are formatted consistently.
• Suggestions of possible actions need to be taken from the summarized outcomes of a situation.
• In these tables, semi-standardized languages might be used.
• Table users are not necessarily know how to use a computer.

Drawbacks:

• Decision tables are not well suited to large-scale applications. There is a  requirement of splitting huge tables into smaller ones to eliminate redundancy.
• The complete sequence of actions is not reflected in the decision tables.
• A partial solution is presented.

2. Decision Tree: A decision tree is a graph that always uses a branching method in order to demonstrate all the possible outcomes of any decision. Decision Trees are graphical and show a better representation of decision outcomes. It consists of three nodes namely Decision Nodes, Chance Nodes, and Terminal Nodes.

Types of the decision tree:

• Categorical variable decision tree
• Continuous variable decision tree

Benefits:

• A decision tree is simple to comprehend and use.
• New scenarios are simple to add.
• Can be combined with other decision-making methods.
• Handling of both numerical and categorial variables
• The classification does not require many computations.
• Useful in analyzing and solving various business problems.

Drawbacks:

• They are inherently unstable, which means that a slight change in the data can have a result in a change in the structure of the optimal decision tree, and they are frequently wrong.
• These are less suitable for estimation tasks where the outcome required is the value of a continuous variable.
• The alternative options perform better with the same data. A random forest of decision trees can be used as a replacement but it is not as straightforward to comprehend as a single decision tree.
• Calculations can become quite complicated, especially when several values are uncertain and/or multiple outcomes are related.

### Difference between Decision Table and Decision Tree:

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