引导式测验 · 中等 等级

可解释的 AI 和 SHAP 测验

查看 SHAP 属性模型输出的方式、局部图和汇总图显示的内容，以及为什么需要仔细解释解释。

相关引导路径可解释的人工智能和形状
问题 1 的 8

SHAP 值基于什么数学概念？

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  1. SHAP 值基于什么数学概念？
    • 合作博弈论的沙普利值
    • 贝叶斯后验
    • 梯度下降
    • 主成分分析
  2. SHAP 的“可加性”（局部精度）属性意味着对于单个预测，SHAP 值...
    • 都是积极的
    • 预测与基线输出之间的差值之和
    • 标准化为 0 到 1 之间
    • 等于模型的训练精度
  3. 为什么计算精确的 Shapley 值通常很昂贵？
    • 需要多次重新训练模型
    • 每个预测都需要标记的测试数据
    • 它必须平均一个特征对所有可能的特征子集的边际贡献
    • 它仅适用于 GPU
  4. 哪种 SHAP 变体可以有效地计算梯度增强树模型的精确值？
    • 内核SHAP
    • 深形状
    • 线性形状
    • 树形
  5. Why do teams use explanation methods such as SHAP during model development?
    • To inspect model behavior, investigate influential features, and support review
    • Because SHAP proves the model is fair
    • Because every law requires SHAP for every model
    • Because feature attributions establish causal effects
  6. What can a SHAP summary plot show across a dataset?
    • Whether a feature caused the outcome in the real world
    • How much features affect model outputs across examples, including the spread of their SHAP values
    • Which individual training row leaked the target label
    • A guaranteed ranking of features for every possible dataset
  7. What does a local SHAP explanation describe?
    • A causal effect of changing a real-world feature
    • A guarantee that the model is fair
    • Feature attributions for one prediction relative to a chosen baseline and feature convention
    • The global training accuracy for every dataset
  8. Why can correlated features complicate SHAP interpretation?
    • Correlated features make all SHAP values exactly zero
    • Correlation proves one feature caused the prediction
    • SHAP does not use a model output
    • Different assumptions about feature dependence can change how contributions are allocated