FAQ: Decision Trees - Weighted Information Gain

#1

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This community-built FAQ covers the “Weighted Information Gain” exercise from the lesson “Decision Trees”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Data Science

Machine Learning

FAQs on the exercise Weighted Information Gain

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#2

How is defined the quantity of splitted sets. In the lesson’ example it is 3. Though, in the function split(cars, car_labels, 3) - 3 is the index of the column and not the quantity of sets.

#3

Hi there!
I have troubles understanding understanding the split function given in this exercise (although it isn’t explained anywhere).

def split(dataset, labels, column):
    data_subsets = []
    label_subsets = []
    counts = list(set([data[column] for data in dataset]))
    counts.sort()
    for k in counts:
        new_data_subset = []
        new_label_subset = []
        for i in range(len(dataset)):
            if dataset[i][column] == k:
                new_data_subset.append(dataset[i])
                new_label_subset.append(labels[i])
        data_subsets.append(new_data_subset)
        label_subsets.append(new_label_subset)
    return data_subsets, label_subsets

Could someone explain the code to me?

Thanks in advance!