DashAI.back.explainability.KernelShap
- class KernelShap(model: BaseModel, link: str = 'identity')[source]
Kernel SHAP is a model-agnostic explainability method for approximating SHAP values to explain the output of machine learning model by attributing contributions of each feature to the model’s prediction.
- __init__(model: BaseModel, link: str = 'identity')[source]
Initialize a new instance of a KernelShap explainer.
- Parameters:
model (BaseModel) – Model to be explained.
link (str) – String indicating the link function to connect the feature importance values to the model’s outputs. Options are ‘identity’ to use identity function or ‘logit’to use log-odds function.
Methods
__init__(model[, link])Initialize a new instance of a KernelShap explainer.
explain_instance(instances)Method for explaining the model prediciton of an instance using the Kernel Shap method.
fit(background_dataset[, ...])Method to train the KernelShap explainer.
get_schema()Generates the component related Json Schema.
plot(explanation)Method to create the explanation plot using plotly.
validate_and_transform(raw_data)It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.
Attributes
COLORCOMPATIBLE_COMPONENTSDISPLAY_NAMETYPE