PrimaryApproachestoLocalInterpretation.md

Primary Approaches to Local Interpretation

source: https://bradleyboehmke.github.io/HOML/iml.html

Local Interpretable Model-Agnostic Explanations

Assumptions:

• Every model is linear on a local scale;
• It is possible to fit a simple surrogate model around a single observation that will mimic how the global model behaves at the locality;

Algorithm:

1. Permute training data to create replicated feature data with slight value modifications;

2. Compute proximity measure (1 - distance) between observation of interest and each of the permuted observations;

3. Apply selected machine learning model to predict outcomes of permuted data;

4. Select m number of features to best describe predicted outcomes; (Forward selection, ridge or lasso regression, decision tree)

5. Fit a sample model to the permuted data, explaining the complex model outcome with m features from the permuted data weighted by its similarity to the original observation;

6. Use the resulting feature weights to explain local behaviour;

Shapley Values

Explaining indiividual predictions by borrowing ideas from coalitional game theory, Shapley Values.

Localized Step-Wise Produces

Partial dependence algorithm with added step-wise procedure.