Changelog

Source: NEWS.md

ingredients 0.4

• CRAN release

ingredients 0.3.12

• aspect_importance is moved to DALEXtra (#66)
• examples are updated in order to reflect changes in titanic_imputed from DALEX (#65)

ingredients 0.3.11

• modified plot.aspect_importance - it can plot more than single figure
  
• modified triplot, plot.aspect_importance and plot_group_variables to add more clarity in plots and allow some parameterization

ingredients 0.3.10

• added triplot function that illustrates hierarchical aspect_importance() groupings
• changes in aspect_importance() functions
• added back the vigniette for aspect_importance()

ingredients 0.3.9

• change only_numerical parameter to variable_type in functions aggregated_profiles(), cluster_profiles(), plot() and others, as requested in #15

ingredients 0.3.8

• Natural language description generated with describe() function for ceteris_paribus(), feature_importance() and aggregated_profiles() explanations.

ingredients 0.3.7

• aggregated_profiles_conditional and aggregated_profiles_accumulated are rewritten with some code fixes

ingredients 0.3.6

• a new version of lime is implemented in the lime()/aspect_importance() function.
• Kasia Pekala and Huber Baniecki are added as contributors.

ingredients 0.3.5

• new feature #29. Feature importance now takes an argument B that replicates permutations B times and calculates average from drop loss.

ingredients 0.3.4

• plotD3 now supports Ceteris Paribus Profiles.
• feature_importance now can take variable_grouping argument that assess importance of group of features
• fix in ceteris_paribus, now it handles models with just one variable
• fix #27 for multiple rows

ingredients 0.3.3

• show_profiles and show_residuals functions extend Ceteris Paribus Plots.
• show_aggreagated_profiles is renamed to show_aggregated_profiles
• centering of ggplot2 title

ingredients 0.3.2

• added new functions describe() and print.ceteris_paribus_descriptions() for text based descriptions of Ceteris Paribus explainers
• plot.ceteris_paribus_explainer works now also for categorical variables. Use the only_numerical = FALSE to force bars

ingredients 0.3.1

• added references to PM VEE
• partial_profiles(), accumulated_profiles() and conditional_profiles for variable effects
• major changes in function names and file names

ingredients 0.3

• ceteris_paribus_2d extends classical ceteris paribus profiles
• ceteris_paribus_oscillations calculates oscilations for ceteris paribus profiles
• fixed examples and file names

ingredients 0.2

• cluster_profiles helps to identify interactions
• partial_dependency calculates partial dependency plots
• aggregate_profiles calculates partial dependency plots and much more

ingredients 0.1

• port of model_feature_importance and model_feature_response from DALEX to ingredients
• added tests