The Gini Index, otherwise called the Gini Impurity or Gini Coefficient, is a significant impurity measure utilized in decision tree algorithms. In this article, we will investigate the idea of Gini Index exhaustively, its numerical formula, and its applications in machine learning.

In economics, the Gini coefficient (/ ˈdʒiːni / JEE-nee), also known as the Gini index or Gini ratio, is a measure of statistical dispersion intended to represent the income inequality, the wealth inequality, or the consumption inequality [2] within a nation or a social group. It was developed by Italian statistician and sociologist Corrado Gini.

2022年11月24日 · Explore the Gini Index in machine learning, its role in decision trees, and how it's calculated. Learn about Gini impurity, the Gini coefficient formula, and related concepts like information gain and entropy.

Although classification accuracy is a good measure for classification performance, a popular cost function (splitting criterion) for classification trees is the Gini index, a measure of the impurity of a region (not just feature regions in machine learning, but also regions in society in economics, e.g. to measure income/wealth inequality).

The Gini Index (or Gini Impurity) is a widely employed metric for splitting a classification decision tree. In the following sections, you’ll explore its definition, mathematical formula, its role in tree construction, and a step-by-step example demonstrating how it is computed.

2023年6月30日 · The Gini coefficient, or Gini index, is the most commonly used measure of inequality. It was developed by Italian statistician Corrado Gini (1884–1965) and is named after him. It is typically used as a measure of income inequality, but it can be used to measure the inequality of any distribution — such as the distribution of wealth or even ...

2023年1月29日 · To do this, we will calculate the Gini impurity for each feature and select the feature with the lowest Gini impurity. First, we will calculate the Gini impurity of the target class label...

The Gini index is a measure of impurity or inequality often used in decision tree algorithms for evaluating the quality of a split. It quantifies the extent to which a split divides the target variable (class labels) unevenly across the resulting branches.

We distinguish class inequality from individual inequality, the type that is better measured by the Gini. Once the classes are estimated, the number of estimated classes obtained from the best fitting model facilitates the decomposition of the Gini index into individual and class inequality.

2024年4月14日 · The Gini index determines a nation's level of income inequality by measuring the income distribution or wealth distribution across its population. The Gini index was developed in 1912 by...