What does the GINI coefficient measure?

The GINI coefficient primarily measures statistical dispersion, commonly used to assess inequality within a distribution, such as income or wealth among a population. In the context of analytics and modeling, when one considers its application as a metric for understanding the effectiveness of a model, it can be seen as an indicator of how well a binary classification model distinguishes between classes.

In model evaluation, particularly in classification tasks, the GINI coefficient is derived from the Lorenz curve, which plots the cumulative distribution of income or wealth. A model with a high GINI coefficient indicates that it can effectively separate positive classes from negative ones, demonstrating that the model has a strong predictive capability in identifying outcomes within a dataset.

The other options do not accurately capture the essence of what the GINI coefficient measures. While option A links to the concept of predictiveness, the other choices relate to different statistical measures, such as the accuracy of predictions or correlations between variables, which are not the primary focus of the GINI coefficient. Thus, understanding the GINI coefficient in the context of model effectiveness provides valuable insight into its application in analytics.