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Compute Local Model-agnostic Explanations (LIMEs)

LIMEMachine learning interpretabilityMliExplanationInstance-level explanation
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jamestsai profile image
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Jun 21, 2019 1:19 PM
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這是使用LIME計算說明的示例。 選擇了XGBoost模型,但是可以使用任何模型及其Learner和Predictor節點集。 -閱讀有關葡萄酒的數據集 -在訓練和測試中對數據進行分區 -選擇一些測試集實例行進行解釋 -在輸入表中為每個實例創建本地樣本(LIME Loop Start) -使用預測變量節點和訓練有素的模型對樣本進行評分 -計算LIME,即通過使用樣本訓練局部GLM並提取權重來進行局部模型不可知的解釋。 -在組合視圖中可視化它們(右鍵單擊>打開視圖)

External resources

  • Data Source: UCI - Wine Quality Data Set
  • LIME - Local Interpretable Model-Agnostic Explanations, Marco Tulio Ribeiro, Blog Post
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Created with KNIME Analytics Platform version 4.1.3
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    KNIME XGBoost IntegrationTrusted extension

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    Version 4.1.0

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