Machine learning, please solve this asap

a Q3. [2 points] In a binary classification problem, assume we have a classifier R that guesses randomly (50/50 positiveegative). For data that has 70% positive and 30% negtive instances, show that R gets 50% error. Q4. a. [3 points] Change Algorithm 1 (in chapter 1 page 13) in the textbook to take a maximum depth parameter d. b. [1 point] Do we also need to change Algorithm 2 (page 3)? Why? 1 1: 5 o else T: Algorithm 1 DECISIONTREETRAIN(data, remaining features) guess + most frequent answer in data // default answer for this data If the labels in data are unambiguous then return Lear(guess) // base case: no need to split further + else if remaining features is empty then return Leaf(guess) // base case: cannot split further // we need to query more features for all f e remaining features do NO the subset of data on which f=no YES + the subset of data on which f=yes score[f] # of majority vote answers in NO + # of majority vote answers in YES // the accuracy we would get if we only queried on end for ft the feature with maximal score(f) NO + the subset of data on which f=no YES the subset of data on which f=fjes left DecisionTREETRAIN(NO, remaining features \ }) right + DecisionTREETRAIN(YES, remaining features \ {f}) return Node, left, right) 9: 100 11: 123 15 14 # at 17 10 end if 3: Algorithm 2 DECISIONTREETEST(tree, test point) * If tree is of the form LEAF(guess) then return guess ; else if tree is of the form Nope, left, right) then iff = no in test point then return DecisionTRELTest(left, test point) else return DecisioNTREETest(right, test point) end it end If + 7