ICEProb

class ordinal_xai.interpretation.ice_prob.ICEProb(model, X, y=None)[source]

Bases: BaseInterpretation

Individual Conditional Expectation (ICE) Plot interpretation method for probabilities.

This class implements ICE plots specifically designed for visualizing probability distributions in ordinal regression models. It uses stacked area plots to show how the probability distribution across ordinal classes changes as feature values change.

Parameters:

  • model (object) – The trained ordinal regression model. Must implement predict_proba method.

  • X (pd.DataFrame) – Dataset used for interpretation. Should contain the same features used during model training.

  • y (pd.Series, optional) – Target labels. Not required for interpretation but useful for reference.

model

The trained ordinal regression model

Type:

object

X

Dataset used for interpretation

Type:

pd.DataFrame

y

Target labels (if provided)

Type:

pd.Series

__init__(model, X, y=None)[source]

Initialize the ICE Plot interpretation method for probabilities.

Parameters:

  • model (object) – The trained ordinal regression model

  • X (pd.DataFrame) – Dataset used for interpretation

  • y (pd.Series, optional) – Target labels

explain(observation_idx=None, feature_subset=None, plot=False)[source]

Generate Individual Conditional Expectation Plots for probabilities.

This method computes and optionally visualizes how the model’s probability distribution changes as feature values change. It uses stacked area plots to show the probability distribution across ordinal classes.

Parameters:

  • observation_idx (int, optional) – Index of specific instance to highlight in the plot. If provided, only this instance’s probability distribution will be shown along with the average (PDP) distribution.

  • feature_subset (list, optional) – List of feature names or indices to plot. If None, all features are used.

  • plot (bool, default=False) – Whether to create visualizations of the ICE plots.

Returns:

Dictionary containing ICE results for each feature: - ‘grid_values’: Feature values used for prediction - ‘average’: Average probability predictions (PDP) for each class - ‘individual’: Individual probability predictions for each instance and class

Return type:

dict

Notes

  • Uses stacked area plots to visualize probability distributions

  • Shows both individual instance probabilities and average probabilities

  • Includes probability annotations at original feature values

  • Uses a viridis colormap for different ordinal classes

  • Automatically handles both categorical and numerical features

_abc_impl = <_abc._abc_data object>