Impureza de Gini (2 classes)
- Created by
- Renato Passos, Eng. de Software
- Reviewed by
- Renato Passos, Eng. de Software
Last updated: Apr 18, 2026
About this calculator
The Gini Impurity is a statistical measure used to evaluate the quality of a binary classification. It is calculated as 1 minus the sum of the squares of the fractions of instances classified in each class.
This measure is especially useful in machine learning problems where the accuracy of a classification is crucial. The Gini Impurity helps to identify which problems are the most difficult to solve.
Keep in mind that the Gini Impurity is highly dependent on the data distribution. Therefore, it is essential to consider this measure in conjunction with other metrics to obtain a comprehensive view of the classification quality.
Frequently asked questions
What is the Gini Impurity?
The Gini Impurity is a statistical measure that evaluates the quality of a binary classification. It is calculated as 1 minus the sum of the squares of the fractions of instances classified in each class.
When to use the Gini Impurity?
The Gini Impurity is useful in machine learning problems where the accuracy of a classification is crucial.
How to calculate the Gini Impurity?
The Gini Impurity is calculated as 1 minus the sum of the squares of the fractions of instances classified in each class.
What does a high Gini Impurity mean?
A high Gini Impurity indicates that the classification is poor and can be improved.