I am building a decision tree from scratch. I have been using entropy so far (calculated this way):

def calculate_entropy(data):

    label_column = data[:,-1]
    _,counts = np.unique(label_column,return_counts=True)

    probabilities = counts/counts.sum()
    entropy = sum(probabilities*(-np.log2(probabilities)))

    return entropy

Now I want to implement a function that calculates the Gini index of an array. From here I get the following function that calculates:

def gini_coeff(x):
    # requires all values in x to be zero or positive numbers,
    # otherwise results are undefined
    n = len(x)
    s = x.sum()
    r = argsort(argsort(-x)) # calculates zero-based ranks
    return 1 - (2.0 * (r*x).sum() + s)/(n*s)

That for

x = [1,1,2]


But from this other source I see that the Gini impurity can be calculated

$ G = \sum_i^n \cdot p(i) \cdot (1-p(i))$

That I implemented this way

def calculate_gini(data):
    label_column = data
    _,counts = np.unique(label_column,return_counts=True)

    probabilities = counts/counts.sum()
    return sum(probabilities*(1-probabilities))



I believe that I am making here a conceptual problem. What is it? What are the differences? Which one should I use for a decision tree?


1 Answer 1


The Gini coefficient measures dispersion of non-negative values in such a fashion that Gini coefficient = 0 describes perfect equality (zero variation of values), and Gini coefficient = 1 describes 'maximal inequality' where all but one individuals (units, etc.) have value zero, and all non-zero value is concentrated in a single individual. This was developed in an economic context to describe income distribution inequality.

By contrast the Gini impurity measures "how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the subset." If your work is CART based (as your tag suggests), you are interested in the Gini impurity, not the Gini coefficient.

The Gini impurity for a single label $i$ is the probability of that item occurring time the probability of some other label $\ne i$ occurring, which gives $p_{i}(1-p_{i})$. However, the Gini impurity for a set of labels ($J$ many labels) is the sum of this $p_{i}(1-p_{i})$ term across all labels, which simplifies to $1-\sum_{i=1}^{J}{p_{i}^{2}}$

Your code seems, to my admittedly weak coding eye, to capture the definition for a set of labels.

  • 1
    $\begingroup$ Thanks! I was confused. My second implementation will be good then?(calculate_gini) $\endgroup$ Commented Jan 22, 2020 at 17:18
  • 2
    $\begingroup$ (+1) Corrado Gini was prolific and his name is attached to yet more measures, but @Alexis shows good taste in stopping there. $\endgroup$
    – Nick Cox
    Commented Jan 22, 2020 at 17:29
  • 1
    $\begingroup$ High praise, @NickCox ! Yeah, I did not even gush over just how cool the Gini coefficient is as a measure of income inequality (as contrasted with other such measures). Such good taste I have. ;) $\endgroup$
    – Alexis
    Commented Jan 22, 2020 at 17:31
  • 1
    $\begingroup$ Note that the complement $\sum p_i^2$ is also often used. $\endgroup$
    – Nick Cox
    Commented Jan 22, 2020 at 17:31

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