Explaining Correlated Features in Machine Learning Models
Calculates importance of variable groups (called aspects) for a select...
Aspects importance for single aspects
Calculate triplot that sums up automatic aspect/feature importance gro...
Creates a cluster tree from numeric features
Function for getting binary matrix
Helper function that combines clustering variables and creating aspect...
Calculates importance of hierarchically grouped aspects
Cuts tree at custom height and returns a list
Function for plotting aspect_importance results
Plots tree with correlation values
Plots triplot
Function for printing aspect_importance results
Tools for exploring effects of correlated features in predictive models. The predict_triplot() function delivers instance-level explanations that calculate the importance of the groups of explanatory variables. The model_triplot() function delivers data-level explanations. The generic plot function visualises in a concise way importance of hierarchical groups of predictors. All of the the tools are model agnostic, therefore works for any predictive machine learning models. Find more details in Biecek (2018) <arXiv:1806.08915>.
Useful links