Decision Tree:
Decision tree is a data mining technique used for classifying variables that are co-related
to both dependent and independent variables.
Helps classify the data into branch-like sections starting from the root node, internal, and
leaf nodes.
It effectively presents the necessary information to the user.
It is amongst the most supervised machine learning algorithms and is used for regression
analysis and problems related to the classification of variables
Decision Tree can be better understood with the help of an example:
Alan has a job offer of $25,000 and he has to decide whether he should accept the offer
or not.
Alan’s decision to take the new role or not would not only depend on the salary offered,
but other convenience like travel time, food and travelling expenses provided by the company.
Let us look at the possibilities:
So how do the players find a solution to the game?
Stage 1: If Alan gets a $25,000 salary, then he would look into other criteria which includes
his travelling time else reject the offer.
Stage 2: As per Alan, his maximum travelling time should be of 1 hour. If the travelling
time meets his expectations, he would then look into the perks offered by the company
else would reject the offer.
Stage 3: Once, Alan’s second criteria is met, he would also consider other perks like
food and travelling expenses. If the company provides these perks, Alan will accept the
offer else reject it.
Conclusion:
It would be best for Alan to accept the offer when his criteria of getting a salary of $25,000
and perks like food and travelling expenses are met
and also, he could save
his travelling time.