$\backslash$table$[[\backslash$table$[[$GLCM$],[$Horizontal$],[$Variance$]],\backslash$table$[[$GLCM Vertical$],[$Variance$]],\backslash$table$[[$ASM$],[($or Energy$)],[$for VerticalcIcM$]],\backslash$table$[[$Fruit Quality$],[($Target$)]],],[$High$,$High,High,High,$\backslash$table$[[$Training$],[$Data$]]],[$High$,$Low,Low,Medium$],[$Low$,$Low,Low,Low$],[$Low$,$High,Low,Low$],[$High$,$Low,High,Medium$],[$High$,$Low,High,High$],[$Low$,$Low,Low,Medium$],[$Low$,$Low,Low,Medium$],[$Low$,$High,Low,Low,Test Data$],[$High$,$High,High,High$],[$High$,$High,High,Medium$],[$Low$,$High,Low,Medium$]]$

A$.$ If the decision tree is constructed using ID$3$ classifier with entropy based information gain as a criterion for attribute selection, Identify the attribute used at the root. Show all the calculations involved. Draw the resultant tree after the first iteration.

B$.$ Consider the decision trees given below in the table. Which of the trees is more suitable for the given data? Use precision of class $=$ "High" and Recall of class $=$ "Medium" as the decision criteria and explain with necessary computations.