DashAI.back.explainability.PermutationFeatureImportance
- class PermutationFeatureImportance(model: BaseModel, scoring: str | List[str] | None = None, n_repeats: int = 5, random_state: int | None = None, max_samples_fraction: float = 0.5)[source]
Permutation Feature Importance is a explanation method to asses the importance of each feature in a model by evaluating how much the model’s performance decreases when the values of a specific feature are randomly shuffled.
- __init__(model: BaseModel, scoring: str | List[str] | None = None, n_repeats: int = 5, random_state: int | None = None, max_samples_fraction: float = 0.5)[source]
Methods
__init__(model[, scoring, n_repeats, ...])explain(dataset)Method for calculating the importance of features in the model.
get_schema()Generates the component related Json Schema.
plot(explanation)Method to create the explanation plot.
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