Decision Tree : Example

Decision Tree learning Algorithm:

(i)           Choose an attribute from the dataset

(ii)          Calculate the significance of the attribute in the splitting of the data.

(iii)        Split data based on the value of the best attribute.

(iv)        Go to step (i)

To understand it, Let’s consider a data set of patients given with their ID, age, sex, BP, Cholesterol, and Drug is given to them according to their diseases. The last entry is given only values but the drug is not given. Our task is to find the drug for a patient with ID P15.

Patient ID	Age	Sex	BP	Cholesterol	Drug( A or B)
P1	Young	F	High	Normal	A
P2	Young	F	High	High	A
P3	Middle	F	High	Normal	B
P4	Senior	F	Normal	Normal	B
P5	Senior	M	Low	Normal	B
P6	Senior	M	Low	High	A
P7	Middle	M	Low	High	B
P8	Young	F	Normal	Normal	A
P9	Young	M	Low	Normal	B
P10	Senior	M	Normal	Normal	B
P11	Young	M	Normal	High	B
P12	Middle	F	Normal	High	B
P13	Middle	M	High	Normal	B
P14	Senior	F	Normal	High	A
P15	Middle	F	Low	Normal	?

Let us make a decision about the tree for the given problem.

Based on this classification, we can say that P15 need Drug B.