def accuracy_test(rows):
"""Performs cross-out-one validation methods on the dataset passed."""
# Removing the label
rows = rows[1:]
total_accuracy = 0.0
total_elements = len(rows)
for i, row in enumerate(rows):
singled_out = row
rows.remove(row)
remaining_rows = rows
actual_label = row[-1]
dt = DecisionTree(remaining_rows)
node = dt.build_tree()
#node = build_tree(remaining_rows)
result = dt.classify(singled_out, node)
# either 'yes' or 'no' prediction
if len(result) == 1:
if actual_label in result:
total_accuracy += 1
continue
else:
total_accuracy += 0
continue
# both 'yes' and 'no' prediction
else:
prob_correct = result[actual_label]
if actual_label == 'yes':
incorrect_label = 'no'
else:
incorrect_label = 'yes'
prob_incorrect = result[incorrect_label]
total_prob = prob_correct + prob_incorrect
accuracy_for_this_test = prob_correct / total_prob
total_accuracy += accuracy_for_this_test
rows.append(singled_out)
final_accuracy = (total_accuracy / total_elements)
return final_accuracy
print(
"You can randomly choose a data point, and this program can classify")
print(
"that data point for you. Hit 1 if you want to test a random example")
print("Hit any key to skip.")
will_rand = input()
while True:
if will_rand != '1':
break
chosen = random.randint(1, len(data) - 1)
print("\nExample we will classify ", data[chosen])
actual = data[chosen][-1]
print("Actual label on this example is **{}**".format(actual))
c = t.classify(row=data[chosen])
print("The prediction based on the classifier is: ")
print("Prediction: confidence % =", t._prediction(c))
print(
"\n\nEnter 1 if you want to play again. Hit `enter` if you are done."
)
play_again = input()
if play_again != '1':
break
print("To check the accuracy test using cross validation, hit 1")
print("Hit `enter` to skip accuracy testing.")
acc_test = input()
if acc_test == '1':
