Gini Impurity is a metric used in decision tree algorithms to measure the impurity or disorder of a dataset, with a lower value indicating a more homogeneous node. It is calculated as the probability of a randomly chosen element being incorrectly classified if it was randomly labeled according to the distribution of labels in the subset.

Gini Impurity

decision tree algorithms

Impurity refers to the presence of unwanted substances or elements within a material, which can significantly alter its properties and behavior. Understanding and controlling impurities is crucial in fields like chemistry, materials science, and pharmacology, where purity directly impacts the efficacy, safety, and performance of products.

Impurity

disorder of a dataset

homogeneous node

probability of a randomly chosen element

incorrectly classified

randomly labeled

distribution of labels

A subset is a set whose elements are all contained within another set, allowing for the comparison and analysis of relationships between different sets. Understanding subsets is fundamental in set theory, as it lays the groundwork for operations like unions, intersections, and complements, and is crucial for topics in mathematics and computer science.

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