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LIME
Machine learning Explain Explanation Instance-level explanation Interpret Machine learning interpretability Mli Model Reason code
  1. Workflow
    Model Interpretability, Titanic
    Titanic Random Forest ML Interpretability
    +6
    The workflow demonstrates how to use SHAP, Shapley Values and LIME implemenatations in KNIME 4.0 and generates a basic combined v…
    knime > Examples > 04_Analytics > 17_Machine_Learning_Interpretability > 03_Titantic_Prediction_Explanations
    4
    knime
  2. Workflow
    Compute Local Model-agnostic Explanations (LIMEs)
    LIME Machine learning interpretability Mli
    +9
    This is an example for computing explanation using LIME. An XGBoost model was picked, but any model and its set of Learner and Pr…
    knime > Examples > 04_Analytics > 17_Machine_Learning_Interpretability > 01_Compute_LIMEs
    1
    knime
  3. Workflow
    Compute Local Model-agnostic Explanations (LIMEs)
    LIME Machine learning interpretability Mli
    +9
    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（LI…
    jamestsai > Public > 01_Compute_LIMEs
    0
    jamestsai
  4. Workflow
    LIME Loop Nodes with a Custom Regression Model
    LIME Machine learning interpretability Mli
    +10
    An XGBoost Tree Ensemble Regression model was picked, but any model and its set of Learner and Predictor nodes can be used. - Rea…
    knime > XAI Space > Regression > Custom Models > 03_Compute_LIMEs
    0
    knime
  5. Workflow
    LIME Loop Nodes with AutoML
    LIME Machine learning interpretability Mli
    +9
    This is an example for computing explanation using LIME. AutoML component was used to pick the best model, but any model and its …
    knime > XAI Space > Classification > AutoML > 07_Compute_LIMEs
    0
    knime
  6. Workflow
    LIME Loop Nodes with a Custom Model
    LIME Machine learning interpretability Mli
    +9
    This is an example for computing explanation using LIME. An XGBoost model was picked, but any model and its set of Learner and Pr…
    knime > XAI Space > Classification > Custom Models > 07_Compute_LIMEs
    0
    knime
  7. Workflow
    Model Interpretability, Titanic
    Titanic Random Forest ML Interpretability
    +6
    The workflow demonstrates how to use SHAP, Shapley Values and LIME implemenatations in KNIME 4.0 and generates a basic combined v…
    joelbec > Public > 03_Titantic_Prediction_Explanations
    0
    joelbec
  8. Workflow
    LIME Loop Nodes with AutoML (Regression)
    LIME Machine learning interpretability Mli
    +10
    This is an example for computing explanation using LIME. AutoML (Regression) component is used to select the best model, but any …
    knime > XAI Space > Regression > AutoML > 03_Compute_LIMEs
    0
    knime