Problema
Decision trees split data into groups that are as pure as possible. Gini impurity measures how mixed a group's labels are:
Gini = 1 - Σ pₖ²
where pₖ is the fraction of labels equal to class k. A group with a single class has impurity 0; an even mix of two classes has impurity 0.5.
Implement gini_impurity(labels).
Input
labels: The class labels in the group.
Output
Return the Gini impurity as a decimal number.
Restricciones
1 ≤ length ≤ 10,000
Each label is 1 to 20 characters
Ejemplos
Ejemplo 1
Entrada:
labels = ["yes", "yes", "no", "no"]
Salida esperada:
expected = 0.5Entrada:
std::vector<std::string> labels = {"yes", "yes", "no", "no"};
Salida esperada:
double expected = 0.5;Entrada:
string[] labels = new string[] {"yes", "yes", "no", "no"};
Salida esperada:
double expected = 0.5;Explicación
Half are yes and half are no: 1 - (0.5² + 0.5²) = 1 - 0.5 = 0.5, the most mixed a two-class group can be.
Ejemplo 2
Entrada:
labels = ["cat", "cat", "cat"]
Salida esperada:
expected = 0.0Entrada:
std::vector<std::string> labels = {"cat", "cat", "cat"};
Salida esperada:
double expected = 0.0;Entrada:
string[] labels = new string[] {"cat", "cat", "cat"};
Salida esperada:
double expected = 0.0;Explicación
Only one class: 1 - 1² = 0. The group is pure.
Referencias
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