2. Instance Attribute 1 1 po Attribute 2 Class Attribute 3 1 T T 2 3 T N 4 F 5 E 6 F 7 F 8 N 8 9 Figure 1: Training Dataset Review the table labeled Figure 1: Training Dataset. Assume that we want to use a decision tree for modeling the data. Using entropy as the measure of node impurity, what is the information gain if a split is done individually on Attribute and Attribute 2 Attribute 1 -0.229. Attribute 2 - 0.991 JO Attribute 1 -0.991, Attribute 2-0.007 Attribute 1 -0.229. Attribute 2 -0.007 Attribute 1 -0.007, Attribute 2-0.229 3. Instance Attribute 1 Attribute 2 Attribute 3 Class 1 T T 1 2 T T Y 3 T N 4 F F Y 5 F T IN 6 F 3 N F co N 8 9 F S N Figure 1: Training Dataset Review the table labeled Figure 1: Training Dataset. Assume that we want to use a decision tree for modeling the data. Based on the information provided in the table. Attribute 3 is continuous. Therefore, to apply a split to Attribute 3 a threshold point needs to be identified. Using entropy as the measure of node impurity and the information gain metric, what is the best split point for Attribute 3? A split point equal to 6.5 . A split point equal to 5.5 A split point equal to 3.5 A split point equal to 2.0