Develop an algorithm for classification using decision trees. What is the entropy of this collection of training examples with respect to the target function classification? what is the information gain of a$1$ and a$2$ relative to the training examples?

Draw a decision tree for the given data set?

Instance Classification A$1$ A$2$

$\backslash$table$[[1,+,$T T$],[2,+,$T T$],[3,-,$F F$],[4,-,$T F$],[5,+,$F T$],[6,-,$T F$]]$