Fairness Metrics
Fairness metrics are quantitative measures used to evaluate the fairness of machine learning models. These metrics are used to assess whether a model is biased towards certain groups of people based on their race, gender, age, or other protected characteristics. Fairness metrics can be used to identify and mitigate bias in machine learning models, and to ensure that these models are fair and equitable for all users. Some common fairness metrics include disparate impact, equal opportunity, and demographic parity.
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- Control Unit: In the context of Data Science and Artificial Intelligence, a Control Unit refers to the component of a computer's central processing unit (CPU) that manages the flow of data within the processor. It is responsible for fetching instructions ... Read More >>
- EBNF: EBNF stands for Extended Backus-Naur Form. It is a meta-language used to describe the syntax of programming languages, data formats, and communication protocols. EBNF is an extension of the Backus-Naur Form (BNF) and provides additional syn ... Read More >>
- Time Complexity: Time complexity refers to the amount of time taken by an algorithm to run as a function of the length of the input. It is a measure of the efficiency of an algorithm and is usually expressed using big O notation. The time complexity of an a ... Read More >>
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