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Solve question c) i) ii) iii).

c) Consider the following training dataset where CLASS is the target. Cases Furniture 1 Furniture 2 Furniture 3 Furniture 4 Furniture 5 Furniture 6 Furniture 7 Furniture 8 Density Heavy Heavy Heavy Light Light Heavy Heavy Heavy Grain Small Large Small Large Large Small Large Small Hardness Hard Hard Hard Soft Soft Soft Soft Soft Class Oak Oak Pine Oak Pine Pine Pine Pine i) Calculate the information gain for HARDNESS. If the information gain scores for DENSITY and GRAIN are 0.9387 and 0.9055, respectively, determine which input should be used as the first splitting variable? (4 marks) ii) Build a decision tree using the information obtained in (). (2 marks) ii) Assume Furniture 9 has a missing value "?" for DENSITY. Explain how the missing value is managed during the model training process. (1 mark) c) Consider the following training dataset where CLASS is the target. Cases Furniture 1 Furniture 2 Furniture 3 Furniture 4 Furniture 5 Furniture 6 Furniture 7 Furniture 8 Density Heavy Heavy Heavy Light Light Heavy Heavy Heavy Grain Small Large Small Large Large Small Large Small Hardness Hard Hard Hard Soft Soft Soft Soft Soft Class Oak Oak Pine Oak Pine Pine Pine Pine i) Calculate the information gain for HARDNESS. If the information gain scores for DENSITY and GRAIN are 0.9387 and 0.9055, respectively, determine which input should be used as the first splitting variable? (4 marks) ii) Build a decision tree using the information obtained in (). (2 marks) ii) Assume Furniture 9 has a missing value "?" for DENSITY. Explain how the missing value is managed during the model training process. (1 mark)