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triplot (version 1.3.0)

Explaining Correlated Features in Machine Learning Models

Description

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) .

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Install

install.packages('triplot')

Monthly Downloads

44

Version

1.3.0

License

GPL-3

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Maintainer

Katarzyna Pekala

Last Published

July 13th, 2020

Functions in triplot (1.3.0)

aspect_importance

Calculates importance of variable groups (called aspects) for a selected observation
plot.aspect_importance

Function for plotting aspect_importance results
hierarchical_importance

Calculates importance of hierarchically grouped aspects
list_variables

Cuts tree at custom height and returns a list
calculate_triplot

Calculate triplot that sums up automatic aspect/feature importance grouping
get_sample

Function for getting binary matrix
plot.cluster_variables

Plots tree with correlation values
aspect_importance_single

Aspects importance for single aspects
group_variables

Helper function that combines clustering variables and creating aspect list
cluster_variables

Creates a cluster tree from numeric features
plot.triplot

Plots triplot
print.aspect_importance

Function for printing aspect_importance results