def classify(self, example):
predictions = [tree.classify(example) for tree in self.trees]
counts = defaultdict(int)
for p in predictions:
counts[p] += 1
cls, count = dt_util.get_plurality(counts)
return cls
def evaluate_chi_square(self, chi_square, unsplit_counts):
if chi_square <= CONFIDENCE_BOUND:
return self
print "++ Pruned"
best_class, best_val = dt_util.get_plurality(unsplit_counts)
return decision_tree_leaf(best_class, unsplit_counts)
def classify(self, example):
predictions = [tree.classify(example) for tree in self.trees]
counts = defaultdict(int)
for p in predictions:
counts[p] += 1
cls, count = dt_util.get_plurality(counts)
return cls
