Impurity (Data Science): Revision history

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5 November 2024

  • curprev 06:0406:04, 5 November 2024‎ 핵톤 talk contribs‎ 4,904 bytes +4,904‎ Created page with "In data science, impurity refers to the degree of heterogeneity in a dataset, specifically within a group of data points. Impurity is commonly used in decision trees to measure how "mixed" the classes are within each node or split. A high impurity indicates a mix of different classes, while a low impurity suggests that the data is homogenous or predominantly from a single class. Impurity measures guide the decision tree-building process by helping identify the best featu..." Tag: Visual edit
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