Which method calculates worth based on the product of proportions of individual class outcomes?

The method that calculates worth based on the product of proportions of individual class outcomes is known as Entropy. In the context of decision trees and information theory, Entropy provides a measure of uncertainty or impurity in a dataset. It evaluates how much information is gained by splitting a dataset based on a certain attribute.

When calculating Entropy, the formula considers the proportions of different classes within a particular subset of data. It computes the weighted sum of the logarithms of these proportions, which leads to a value that reflects the level of disorder or randomness in the dataset. A higher Entropy value indicates a greater level of uncertainty regarding class outcomes, while a lower value suggests that the outcomes are more predictable.

In contrast, the Gini Index also measures impurity but does so differently, focusing on the likelihood of incorrectly classifying a randomly chosen element from the set. Variance and Standard Deviation are statistical measures that reflect dispersion in numerical data, and they do not specifically pertain to class outcomes in a classification context like Entropy does. Hence, Entropy is the correct method for calculating worth based on the proportions of individual class outcomes.