# FAQ: Decision Trees - Weighted Information Gain

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

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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.

1 Like

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?

2 Likes

me too. ```
list(set([data[column] for data in dataset]))

``````
what does set do here eactly``````

I think the reason for using `set()` is to eliminate duplicates. The list comprehension creates a list of the values at the index `column` of all `data` in the `dataset`, and the `set()` returns a set object where duplicates are eliminated. We make it a list object again with `list()`.

I think “Method 3” of the following page is easy to understand as an example:

agreed, this just comes out of nowhere, how are we supposed to be able to understand this?

I mean I understand what this function does, create subsets of the data based on specific properties, be it number of doors, how many people the car can hold, size of the trunk, etc.

I just don’t get why has this function be implemented? There is no background provided and just appeared from nowhere.

Hey. I made same research in split function. Try to figure out my explanation There are six (we have 6 features) times we make new data_set, but here the point - we make only one step looking for the best features to find best gain after those step.