ingredients2.3.0 package

Effects and Importances of Model Ingredients

accumulated_dependence

Accumulated Local Effects Profiles aka ALEPlots

aggregate_profiles

Aggregates Ceteris Paribus Profiles

bind_plots

Bind Multiple ggplot Objects

calculate_oscillations

Calculate Oscillations for Ceteris Paribus Explainer

calculate_variable_profile

Internal Function for Individual Variable Profiles

calculate_variable_split

Internal Function for Split Points for Selected Variables

ceteris_paribus

Ceteris Paribus Profiles aka Individual Variable Profiles

ceteris_paribus_2d

Ceteris Paribus 2D Plot

cluster_profiles

Cluster Ceteris Paribus Profiles

conditional_dependence

Conditional Dependence Profiles

describe

Natural language description of feature importance explainer

feature_importance

Feature Importance

partial_dependence

Partial Dependence Profiles

plot.aggregated_profiles_explainer

Plots Aggregated Profiles

plot.ceteris_paribus_2d_explainer

Plot Ceteris Paribus 2D Explanations

plot.ceteris_paribus_explainer

Plots Ceteris Paribus Profiles

plot.ceteris_paribus_oscillations

Plot Ceteris Paribus Oscillations

plot.feature_importance_explainer

Plots Feature Importance

plotD3_aggregated_profiles

Plots Aggregated Ceteris Paribus Profiles in D3 with r2d3 Package.

plotD3_ceteris_paribus

Plots Ceteris Paribus Profiles in D3 with r2d3 Package.

plotD3_feature_importance

Plot Feature Importance Objects in D3 with r2d3 Package.

print.aggregated_profiles_explainer

Prints Aggregated Profiles

print.ceteris_paribus_explainer

Prints Individual Variable Explainer Summary

print.feature_importance_explainer

Print Generic for Feature Importance Object

select_neighbours

Select Subset of Rows Closest to a Specified Observation

select_sample

Select Subset of Rows

show_aggregated_profiles

Adds a Layer with Aggregated Profiles

show_observations

Adds a Layer with Observations to a Profile Plot

show_profiles

Adds a Layer with Profiles

show_residuals

Adds a Layer with Residuals to a Profile Plot

show_rugs

Adds a Layer with Rugs to a Profile Plot

Download source packageRead PDF manual

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) <arXiv:1806.08915>.

  • Maintainer: Przemyslaw Biecek
  • License: GPL-3
  • Last published: 2023-01-15

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