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ML Interpretability
SHAP Titanic Interactive View LIME Random Forest
+5
  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
  2. Node / LoopStart
    SHAP Loop Start
    KNIME Labs ML Interpretability
    SHAP is an acronym for SHapley Additive exPlanations and represents a unified approach to explain the predictions of any machine …
    1
  3. Workflow
    Standardized Coefficients for Regression
    Regression Standardized Coefficients
    +1
    This workflow shows how to better interpret the results of a linear regression model using the Standardized Coefficients componen…
    jaqen79 > Public > Standardized Coefficients for Regression
    0
  4. Workflow
    Model Interpretability: SHAP to explain predictions of Decision Tree
    Titanic ML Interpretability Decision Tree
    +2
    Demonstrates use of SHAP implemenatation in KNIME 4.0 and generates a basic view. Uses Decision Tree model for predicting surviva…
    lisovyi > Public > forum > 18358
    0
  5. 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
  6. Node / Visualizer
    Binary Classification Inspector
    KNIME Labs ML Interpretability
    This node produces a complex view made of four different charts in order to compare, optimize and select predictions of different…
    0
  7. Node / LoopStart
    LIME Loop Start
    KNIME Labs ML Interpretability
    LIME stands for Local Interpretable Model-agnostic Explanations. It tries to explain individual predictions of a black box model …
    0
  8. Node / Visualizer
    Partial Dependence/ICE Plot
    KNIME Labs ML Interpretability
    This node requires the use of the Partial Dependence Pre-processing Component to sample the relevant data. This Component can be …
    0
  9. Node / LoopEnd
    SHAP Loop End
    KNIME Labs ML Interpretability
    Calculates the SHAP values by evaluating the predictions your model made in the loop body. For each explained row of interest (ro…
    0
  10. Node / LoopEnd
    Shapley Values Loop End
    KNIME Labs ML Interpretability
    Aggregates the predictions per row to be explained and calculates the Shapley Values for each feature prediction combination. For…
    0
  11. Node / LoopStart
    Shapley Values Loop Start
    KNIME Labs ML Interpretability
    Shapley Values originated in game theory and in the context of machine learning they have recently became a popular tool for the …
    0