  One of the key elements of explanatory analysis of a predictive model is to
assess the importance of individual variables. Rapid development of the area of
predictive model exploration (also called explainable artificial intelligence
or interpretable machine learning) has led to the popularization of methods for
local (instance level) and global (dataset level) methods, such as
Permutational Variable Importance, Shapley Values (SHAP), Local Interpretable
Model Explanations (LIME), Break Down and so on. However, these methods do not
use information about the correlation between features which significantly
reduce the explainability of the model behaviour. In this work, we propose new
methods to support model analysis by exploiting the information about the
correlation between variables. The dataset level aspect importance measure is
inspired by the block permutations procedure, while the instance level aspect
importance measure is inspired by the LIME method. We show how to analyze
groups of variables (aspects) both when they are proposed by the user and when
they should be determined automatically based on the hierarchical structure of
correlations between variables. Additionally, we present the new type of model
visualisation, triplot, which exploits a hierarchical structure of variable
grouping to produce a high information density model visualisation. This
visualisation provides a consistent illustration for either local or global
model and data exploration. We also show an example of real-world data with 5k
instances and 37 features in which a significant correlation between variables
affects the interpretation of the effect of variable importance. The proposed
method is, to our knowledge, the first to allow direct use of the correlation
between variables in exploratory model analysis.
