Gini Impurity (With Examples)

2 minute read

TIL about Gini Impurity: another metric that is used when training decision trees.

Last week I learned about Entropy and Information Gain which is also used when training decision trees. Feel free to check out that post first before continuing.

I will be referencing the following data set throughout this post

"Will I Go Running" Data Set

Day   | Weather | Just Ate | Late at Work | Will I go Running?
---   | ---     | ---      | ---          | ---
1     | 'Sunny' | 'yes'    | 'no'         | 'yes'
2     | 'Rainy' | 'yes'    | 'yes'        | 'no'
3     | 'Sunny' | 'no'     | 'yes'        | 'yes'
4     | 'Rainy' | 'no'     | 'no'         | 'no'
5     | 'Rainy' | 'no'     | 'no'         | 'yes'
6     | 'Sunny' | 'yes'    | 'no'         | 'yes'
7     | 'Rainy' | 'no'     | 'yes'        | 'no'

Gini Impurity

Gini Impurity is a measurement of the likelihood of an incorrect classification of a new instance of a random variable, if that new instance were randomly classified according to the distribution of class labels from the data set.

Gini impurity is lower bounded by 0, with 0 occurring if the data set contains only one class.

The formula for calculating the gini impurity of a data set or feature is as follows:

G(k) =  Σ P(i) * (1 - P(i))

Where P(i) is the probability of a certain classification i, per the training data set.

If it seems complicated, it really isn’t! I’ll explain with an example.

Example: “Will I Go Running?”

In the data set above, there are two classes in which data can be classified: “yes” (I will go running) and “no” (I will not go running).

If we were using the entire data set above as training data for a new decision tree (not enough data to train an accurate tree… but let’s roll with it) the gini impurity for the set would be calculated as follows:

G(will I go running) = P("yes") * 1 - P("yes") + P("no") * 1 - P("no")

G(will I go running) = 4 / 7 * (1 - 4/7) + 3 / 7 * 1 - P(3/7)

G(will I go running) = 0.489796

This means there is a 48.97% chance of a new data point being incorrectly classified, based on the observed training data we have at our disposal. This number makes sense, since there are more yes class instances than no, so the probability of mis-classifying something is less than a coin flip (if we had the same number).

So how do we use this when building a decision tree?

Gini Gain

Similar to entropy, which had the concept of information gain, gini gain is calculated when building a decision tree to help determine which attribute gives us the most information about which class a new data point belongs to.

This is done in a similar way to how information gain was calculated for entropy, except instead of taking a weighted sum of the entropies of each branch of a decision, we take a weighted sum of the gini impurity.

Gini_Gain(attribute) = total_impurity - impurity_remainder(attribute)

remainder(attribute) = Σ P(attribute_branch_n)*G(branch)

So which should I use? Gini Impurity or Entropy?

It seems that gini impurity and entropy are often interchanged in the construction of decision trees. Neither metric results in a more accurate tree than the other.

All things considered, a slight preference might go to gini since it doesn’t involve a more computationally intensive log to calculate.