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ingredients (version 2.3.0)

Effects and Importances of Model Ingredients

Description

Collection of tools for assessment of feature importance and feature effects. Key functions are: feature_importance() for assessment of global level feature importance, ceteris_paribus() for calculation of the what-if plots, partial_dependence() for partial dependence plots, conditional_dependence() for conditional dependence plots, accumulated_dependence() for accumulated local effects plots, aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles, generic print() and plot() for better usability of selected explainers, generic plotD3() for interactive, D3 based explanations, and generic describe() for explanations in natural language. The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018) .

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Install

install.packages('ingredients')

Monthly Downloads

4,881

Version

2.3.0

License

GPL-3

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Last Published

January 15th, 2023

Functions in ingredients (2.3.0)

plot.aggregated_profiles_explainer

Plots Aggregated Profiles
plot.ceteris_paribus_explainer

Plots Ceteris Paribus Profiles
partial_dependence

Partial Dependence Profiles
plot.ceteris_paribus_oscillations

Plot Ceteris Paribus Oscillations
describe.partial_dependence_explainer

Natural language description of feature importance explainer
feature_importance

Feature Importance
plotD3.aggregated_profiles_explainer

Plots Aggregated Ceteris Paribus Profiles in D3 with r2d3 Package.
plot.ceteris_paribus_2d_explainer

Plot Ceteris Paribus 2D Explanations
plotD3

Plots Ceteris Paribus Profiles in D3 with r2d3 Package.
plot.feature_importance_explainer

Plots Feature Importance
show_profiles

Adds a Layer with Profiles
select_neighbours

Select Subset of Rows Closest to a Specified Observation
show_aggregated_profiles

Adds a Layer with Aggregated Profiles
plotD3.feature_importance_explainer

Plot Feature Importance Objects in D3 with r2d3 Package.
show_observations

Adds a Layer with Observations to a Profile Plot
print.aggregated_profiles_explainer

Prints Aggregated Profiles
show_residuals

Adds a Layer with Residuals to a Profile Plot
print.ceteris_paribus_explainer

Prints Individual Variable Explainer Summary
print.feature_importance_explainer

Print Generic for Feature Importance Object
select_sample

Select Subset of Rows
show_rugs

Adds a Layer with Rugs to a Profile Plot
calculate_oscillations

Calculate Oscillations for Ceteris Paribus Explainer
aggregate_profiles

Aggregates Ceteris Paribus Profiles
ceteris_paribus

Ceteris Paribus Profiles aka Individual Variable Profiles
cluster_profiles

Cluster Ceteris Paribus Profiles
ceteris_paribus_2d

Ceteris Paribus 2D Plot
bind_plots

Bind Multiple ggplot Objects
calculate_variable_split

Internal Function for Split Points for Selected Variables
calculate_variable_profile

Internal Function for Individual Variable Profiles
conditional_dependence

Conditional Dependence Profiles
accumulated_dependence

Accumulated Local Effects Profiles aka ALEPlots