def train_classifier(training_set_list):

    for patient_tuple in training_set_list:
#   if patient_tuple is benign:
#      add attributes from patient_tuple to corresponding benign_sums_list attribute
#      increase the benign_count by 1
#   else:
#      add attributes from patient_tuple to corresponding malignant_sums_list attribute
#      increase the malignant_count by 1
#   create benign_averages_list by dividing each benign_sums_list by the benign_count
#   create malignant_averages_list by dividing each malignant_sums_list by the malignant_count

#   create classifier_list by dividing the sum of each attribute from benign_averages_list
#   and malignantAvg by 2

    return classifier_list