[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["缺少我需要的資訊","missingTheInformationINeed","thumb-down"],["過於複雜/步驟過多","tooComplicatedTooManySteps","thumb-down"],["過時","outOfDate","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["示例/程式碼問題","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2024-08-13 (世界標準時間)。"],[[["Aggregate model performance metrics like precision, recall, and accuracy can hide biases against minority groups."],["Fairness in model evaluation involves ensuring equitable outcomes across different demographic groups."],["This page explores various fairness metrics, including demographic parity, equality of opportunity, and counterfactual fairness, to assess model predictions for bias."],["Evaluating model predictions with these metrics helps in identifying and mitigating potential biases that can negatively affect minority groups."],["The goal is to develop models that not only achieve good overall performance but also ensure fair treatment for all individuals, regardless of their demographic background."]]],[]]
