Sun Burn Decision Tree Construction (ID3 style):

Dataset: 8 examples, 4 attributes + Class {Yes=3, No=5}
Entropy $H(S)= - (3/8)\log_2(3/8) - (5/8)\log_2(5/8)=0.954$

Evaluate each attribute Information Gain.

Similar exhaustive calc shows Hair or Height gives high gain. Manual computation:

• Hair: Blonde (4 ex: 2Y,2N) $H=1$, Brown (3 ex:0Y,3N) $H=0$, Red (1 ex:1Y,0N) $H=0$ → Weighted $=4/8*1=0.5$ → Gain $=0.954-0.5=0.454$
• Weight & Location similar calculations, Hair best initially.

Root: Hair

• Hair=Brown → Leaf No (3/3, pure)
• Hair=Red → Leaf Yes (1/1)
• Hair=Blonde (4 ex remains: Sunita Y, Anita N, Sushma Y, Sweta N) — need further split on remaining attributes (Height, Weight, Location).

Subset Blonde $S_B$: 2Y/2N $H=1$

• Height: Tall 1N, Average 1Y, Short 1Y+1N ($H=1$) → Gain small
• Weight: Light 1Y1N ($H=1$), Average 2 examples 1Y1N → no gain
• Location: No → 2Y (Sunita,Sushma) pure Yes; Yes →2N (Anita,Sweta) pure No → Weighted entropy $0$ → Gain $=1$

So next split Location.

Resulting Tree:

Hair?
├─ Brown → No
├─ Red → Yes
└─ Blonde → Location?
           ├─ No → Yes (Sunita,Sushma)
           └─ Yes → No (Anita,Sweta)

Alternative equivalent tree with Weight/Height may also achieve pure leaves but Location gives perfect split for this subset. This tree classifies all 8 training examples correctly (100% training accuracy).

If using Gini/C4.5, structure similar; root may be Location then Hair depending tie-breaking; any fully consistent tree with 3 leaves is acceptable. Pruning not needed due small data.

Interpretation: For this data, Hair color and absence of Location (No) predicts Sun Burn Yes in blondes.