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Consider the (Mining dataset shown in the following table The last attribute Cheat is the class
attribute. For a three-way -split of these data using attribute Marital Status, compute the
information gain using the entropy function fix measuring impurity. Consider that the three-way
(multi-way) spin a does by using the three possible values Single. Married, and Divorced of the
.Marital Status attribute
Solution
Info(D) = -MI=1pi log2(pi);
Pi= |Ci,D|/|D|
InfoA(D) = vj=1 |Dj|/ |D| *Info(Dj):
Gain(A) = Info(D)-InfoA(D):
Info(D)=- (3/10) log2 (3/10) – (7/10) log2 (7/10)
=0.522+0.361
=0.883
Info Marital status (D) = (4/10)* (-(2/4) log2(2/4) - (2/4) log2(2/4)) + (4/10)*(-(0/4) log2(0/2)-
(4/4) log2(4/4) +(2/10)*(-(1/2) log2(1/2) -(1/2)
log2(1/2))
= 0.4 + 0 + 0.2
= 0.6
Therefore Gain (Marital Status) = 0.883-0.6 = 0.283

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Consider the (Mining dataset shown in the following table The las.pdf

  • 1. Consider the (Mining dataset shown in the following table The last attribute Cheat is the class attribute. For a three-way -split of these data using attribute Marital Status, compute the information gain using the entropy function fix measuring impurity. Consider that the three-way (multi-way) spin a does by using the three possible values Single. Married, and Divorced of the .Marital Status attribute Solution Info(D) = -MI=1pi log2(pi); Pi= |Ci,D|/|D| InfoA(D) = vj=1 |Dj|/ |D| *Info(Dj): Gain(A) = Info(D)-InfoA(D): Info(D)=- (3/10) log2 (3/10) – (7/10) log2 (7/10) =0.522+0.361 =0.883 Info Marital status (D) = (4/10)* (-(2/4) log2(2/4) - (2/4) log2(2/4)) + (4/10)*(-(0/4) log2(0/2)- (4/4) log2(4/4) +(2/10)*(-(1/2) log2(1/2) -(1/2) log2(1/2)) = 0.4 + 0 + 0.2 = 0.6 Therefore Gain (Marital Status) = 0.883-0.6 = 0.283