Decision Table & Decision Tree in Tabular

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