# Convert numerical attributes to binary based on median thresholds
numericalMedians = DecisionTree.setThreshold(trainData)
binaryTrainData = DecisionTree.setBinary(trainData, numericalMedians)
testData = DecisionTree.setBinary(testData, numericalMedians)
#============================================
# AdaBoost
#============================================
print('Running AdaBoost for 1 to 10 iterations...')
myAccuracy = []
maxAccuracy = 0
nt = range(1, 10, 1)
for n in nt:
binaryTrainData1 = copy.deepcopy(binaryTrainData)
binaryTrainData1 = AdaBoost.assignSampleWeights(binaryTrainData1)
# Build stump
stumps = []
stumpWeights = []
iterations = n
newTrainData = binaryTrainData1
weightLookup = None
# Run adaBoost algorithm
for run in range(iterations):
eFeatures = copy.deepcopy(features)
# Build dictionary of feature values
c = 0
for key in eFeatures.keys():
for line in newTrainData:
