MorrisSensitivity#
Link to Algorithm description: Morris Sensitivity Analysis
- class interpret.blackbox.MorrisSensitivity(model, data, feature_names=None, feature_types=None, sampler=None, **kwargs)#
Method of Morris for analyzing blackbox systems. If using this please cite the package owners as can be found here: SALib/SALib
Morris, Max D. “Factorial sampling plans for preliminary computational experiments.” Technometrics 33.2 (1991): 161-174.
Initializes class.
- Parameters:
model – model or prediction function of model (predict_proba for classification or predict for regression)
data – Data used to initialize LIME with.
feature_names – List of feature names.
feature_types – List of feature types.
sampler – A SamplerMixin derrived class that can generate samples from data
**kwargs – Kwargs that will be sent to SALib.analyze.morris.analyze
- explain_global(name=None)#
Provides approximate global explanation for blackbox model.
- Parameters:
name – User-defined explanation name.
- Returns:
An explanation object, visualizes dependence plots.