Difference Between Decision Table & Decision Tree in Tabular Form
|SNO.||Decision Table||Decision Tree|
|01.||A Decision table is a table of rows and columns, separated into four quadrants and is designed to illustrate complex decision rules|
Condition stub, Rules stub, Action stub ,Entries stub
|A Decision tree gives a graphical view of the processing logic involved in decision making and the corresponding actions taken|
|02.||Example: Suppose a technical support company writes a decision table to diagnose printer problems based upon symptoms described to them over the phone from their clients.||Example : A decision tree can be used to classify an example by starting at the root of the tree and moving through it until a leaf node, which provides the classification of the instance.|
|03.||Advantages:The table shows cause and effect relationships.|
2. Tables are of standardized format.
3. Semi – standardized languages can be employed in these tables.
4. Complex tables can easily be split into simpler tables.
5. Table user’s are not required to possess computer knowledge.
|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.
|04.||Disadvantages : a)Decision tables do not scale up well. We need to “factor” large tables into smaller ones to remove redundancy|
b)Total sequence – The total sequence is not clearly shown, i.e., no overall picture is given by decision tables as presented by flowcharts.
c)Logic – Where the logic of a system is simple, flowcharts nearly always serve the purpose better than a decision table.
|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
|05.||Decision table is more compact||But Decision tree is easier to read|