decision tree machine learning advantage and disadvantage of decision tree

 

what is decision tree 

• Logic based algorithms deal with the problem with step by step data streaming with functioning a logic in each step .

 • Here decision tree has been overviewed as a classical example of logic-based algorithm .

 • It is statistical technique used in regression and classification .

 • Decision tree is an example of logically learning super supervised algorithm mainly used for classification that generates a set of decision sequences that if followed will lead to predicting the label of an unlabeled data.

 • A decision supported tool that is represented as a logical tree having a range of conditions and conclusions as nodes and branches that connects the conditions with conclusions.

 • Decision tree takes some decision in every stage of proceeding and considers some alternative choice of action , and among them it selects the most relevant alternatives 

Advantage of decision tree

 • It is a simple method , easy to understand and visualize , fast , requires less data pre-processing , and can deal with both categorical and numerical data .

 • Non-linear parameters don’t effect its performance .

 Disadvantage of decision tree

 • Sometimes this algorithm may lead to a complex tree structure not generalized enough , besides it is quite unstable model .

 • Expensive : the cost of creating decision tree is high since each node requires field sorting .