Aspect module in dalex

In the real world, we come across data with dependencies. It is almost impossible to avoid dependence among predictors when building predictive models.

Unfortunately, many commonly used explainable artificial intelligence (XAI) methods ignore these dependencies, often assuming independence of variables (permutation methods), which leads to unrealistic settings and misleading explanations.

Problems with explaining models based on correlated data is one of the pitfalls described in General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models.

We propose a way in which ML engineers can explain their models taking into account the dependencies between the variables. The first part of the module are functionalities that enable estimating the importance and contribution of variables by grouping them in so called aspects. It is a method inpired by Triplot paper.

In [1]:
import dalex as dx
import numpy as np

import plotly
plotly.offline.init_notebook_mode()