Explaining Correlated Features in Machine Learning Models

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Documentation for package ‘triplot’ version 1.3.0

• DESCRIPTION file.
• Package NEWS.

Help Pages

aspect_importance	Calculates importance of variable groups (called aspects) for a selected observation
aspect_importance.default	Calculates importance of variable groups (called aspects) for a selected observation
aspect_importance.explainer	Calculates importance of variable groups (called aspects) for a selected observation
aspect_importance_single	Aspects importance for single aspects
aspect_importance_single.default	Aspects importance for single aspects
aspect_importance_single.explainer	Aspects importance for single aspects
calculate_triplot	Calculate triplot that sums up automatic aspect/feature importance grouping
calculate_triplot.default	Calculate triplot that sums up automatic aspect/feature importance grouping
calculate_triplot.explainer	Calculate triplot that sums up automatic aspect/feature importance grouping
cluster_variables	Creates a cluster tree from numeric features
cluster_variables.default	Creates a cluster tree from numeric features
get_sample	Function for getting binary matrix
group_variables	Helper function that combines clustering variables and creating aspect list
hierarchical_importance	Calculates importance of hierarchically grouped aspects
lime	Calculates importance of variable groups (called aspects) for a selected observation
list_variables	Cuts tree at custom height and returns a list
model_triplot	Calculate triplot that sums up automatic aspect/feature importance grouping
plot.aspect_importance	Function for plotting aspect_importance results
plot.cluster_variables	Plots tree with correlation values
plot.hierarchical_importance	Calculates importance of hierarchically grouped aspects
plot.triplot	Plots triplot
predict_aspects	Calculates importance of variable groups (called aspects) for a selected observation
predict_triplot	Calculate triplot that sums up automatic aspect/feature importance grouping
print.aspect_importance	Function for printing aspect_importance results
print.triplot	Calculate triplot that sums up automatic aspect/feature importance grouping