Decision Table & Decision Tree
- Decision tables and decision trees are two popular methods used in decision-making and problem-solving.
- The key Difference Between Decision Table and Decision Tree is that Decision Table is just a tabular representation whereas Decision Tree is a graph representation.
- Decision Table and Decision Tree both have different features and functionalities, here is a comparison of some key features between them.
|Decision Table||Decision Tree|
|Representation||Lists out possible conditions||Maps out possible outcomes|
|Format||Grid or matrix||Tree-like structure|
|Rows||Different combinations of conditions||Sequential decisions or conditions|
|Columns||Corresponding actions||Branches of possible outcomes|
|Complexity||Useful for complex scenarios||Useful for simple to moderate scenarios|
|Interdependence||Can account for overlapping or interdependent effects||Not as well-suited for interdependent factors|
|Decision-Making||Deals with all possible conditions and actions in one place||Requires following through branches to determine outcomes|
|Implementation||Can be implemented manually or through software||Typically implemented through software|
|Analysis||Suitable for rule-based analysis||Suitable for probability-based analysis|
- There are two types of decision tables:
- Extendedentry table
- Limitedentry table
Decision Table Advantages:
- Advantages:The table shows cause and effect relationships.
- Tables are of standardized format.
- Semi – standardized languages can be employed in these tables.
- Complex tables can easily be split into simpler tables.
- Table user’s are not required to possess computer knowledge.
Decision Table Disadvantages:
- Decision tables do not scale up well. We need to “factor” large tables into smaller ones to remove redundancy
- Total sequence – The total sequence is not clearly shown, i.e., no overall picture is given by decision tables as presented by flowcharts.
- Logic – Where the logic of a system is simple, flowcharts nearly always serve the purpose better than a decision table.
- There are two types of decision tree.
- Categorical variable decision tree
- Continuous variable decision tree
Decision Tree Advantages:
- A decision tree is easy to understand and interpret.
- Expert opinion and preferences can be included, as well as hard data.
- Can be used with other decision techniques.
- New scenarios can easily be added.
Decision Tree Disadvantages:
- They are unstable, meaning that a small change in the data can lead to a large change in the structure of the optimal decision tree.
- They are often relatively inaccurate. Many other predictors perform better with similar data. This can be remedied by replacing a single decision tree with a random forest of decision trees, but a random forest is not as easy to interpret as a single decision tree.
- Calculations can get very complex, particularly if many values are uncertain and/or if many outcomes are linked