Instance Attribute 1 Attribute 2 Attribute 3 Class 1 point 1 T T Y 2 T T 3 F N 4 5 F 6 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 to calculate Information gain, which attribute will provide the best split Attribute 3 Attribute 1 Attribute 2 Instance 3. instance Attribute 1 Attribute 2 Attribute 3 Class pon 1 1 Y 2 T T 6 5 s 7 O 3 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. Based on the information provided in the table Attribute 3 is continuous. Therefore, to apply a split so Attribute 3. a threshold point needs to be identified. Using entropy as the measure of node impurity and the information gain metria 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 1 1 po 2. Class Instance Attribute 1 Attribute 2 Attribute 3 1 T T 1 2 T T 6 3 T F 5 4 F 4 5 F T 7 ZZZZZ 6 F 3 T 7 F F T 8 F F T 9 5 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 1 and Attribute 22 Attribute 1 -0.229. Attribute 2 - 0.991 Attribute 1 - 0.991. Attribute 2-0.007 Attribute 1 - 0.229. Attribute 2-0.007 Attribute 1-0.007. Attribute 2 - 0.229