# 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: