Movatterモバイル変換
ホーム ingredients 2.2.1 addedfacet_scales parameter toplot.aggregated_profiles_explainer ('free_x' by default)#138 andplot.ceteris_paribus_explainer ('free_x' or'free_y' by default, depending on plot type)#136 ingredients 2.2.02021-04-10 fixes explanations when data has one column#137 ingredients 2.0.12021-02-05 ingredients 2.02020-09-01 plot.ceteris_paribus_explainer now by default for categorical variables plots profiles (not lines -prev default- nor bars)ALE plots are now centered around average y_hat#126 colors from DrWhy color palette is used for CP#125 ingredients 1.3.12020-07-29 defaultsubtitle value inplot.fi changed toNULL fromNA (unification) now in theceteris_paribus function one can specify how grid points shall be calculated, seevariable_splits_type ceteris_paribus and aggregates are now working with missing data, this solves#120 plot(ceteris_paribus) change defaultcolor tolabel orids if more than one profile is detected, this solves#123 ceteris_paribus has now argumentvariable_splits_with_obs which included values fromnew_observations in thevariable_splits, this solves#124 ingredients 1.3.02020-07-01 deprecaten_sample argument infeature_importance (now it’sN)#113 plot_profile now handles multilabel modelsingredients 1.2.02020-04-20 DALEX is moved to Suggests as in#112 plot_categorical_ceteris_paribus can plot bars (again)addbind_plots function ingredients 1.1.0 supportR v4.0 and depend onR v3.5 to comply withDALEX new argumentstitle andsubtitle in several plots ingredients 1.0.0 changedependency todependence#103 ingredients 0.5.2 ceteris_paribus profiles are now working for categorical variablesshow_profiles,show_observations,show_residuals are now working for categorical variablesingredients 0.5.1 synchronisation with changes in DALEX 0.5 new argumentdesc_sorting inplot.variable_importance_explainer#94 ingredients 0.5.02019-12-20 feature_importance now does15 permutations on each variable by default. Use theB argument to change this numberadded boxplots toplot.feature_importance andplotD3.feature_importance that showcase the permutation data inaggregate_profiles: preserve_x_ column factor order and sort its values#82 ingredients 0.4.2 aggregate_profiles use now gaussian kernel smoothing. Use thespan argument for fine control over this parameter (#79 )changevariable_type andvariables arguments usage in theaggregate_profiles,plot.ceteris_paribus andplotD3.ceteris_paribus removevariable_type argument fromplotD3.aggregated_profiles (now the same as inplot.aggregated_profiles) Kasia Pekala is moved as contributor to theDALEXtra asaspect_importance is moved toDALEXtra as well (See v0.3.12 changelog ) added Travis-CI for OSX ingredients 0.4.1 fixed rounding problem in the describe function (#76 ) ingredients 0.42019-10-27 ingredients 0.3.12 aspect_importance is moved toDALEXtra (#66 )examples are updated in order to reflect changes intitanic_imputed fromDALEX (#65 ) ingredients 0.3.11 modifiedplot.aspect_importance - it can plot more than single figure modifiedtriplot,plot.aspect_importance andplot_group_variables to add more clarity in plots and allow some parameterization ingredients 0.3.10 addedtriplot function that illustrates hierarchicalaspect_importance() groupings changes inaspect_importance() functions added back the vigniette foraspect_importance() ingredients 0.3.92019-08-26 changeonly_numerical parameter tovariable_type in functions aggregated_profiles(), cluster_profiles(), plot() and others, as requested in#15 ingredients 0.3.7 aggregated_profiles_conditional andaggregated_profiles_accumulated are rewritten with some code fixesingredients 0.3.6 a new version oflime is implemented in thelime()/aspect_importance() function. Kasia Pekala and Huber Baniecki are added as contributors. ingredients 0.3.5 new feature#29 . Feature importance now takes an argumentB that replicates permutationsB times and calculates average from drop loss. ingredients 0.3.4 plotD3 now supports Ceteris Paribus Profiles.feature_importance now can takevariable_grouping argument that assess importance of group of featuresfix in ceteris_paribus, now it handles models with just one variable fix#27 for multiple rows ingredients 0.3.32019-05-01 show_profiles andshow_residuals functions extend Ceteris Paribus Plots.show_aggreagated_profiles is renamed toshow_aggregated_profilescentering of ggplot2 title ingredients 0.3.2 added new functionsdescribe() print.ceteris_paribus_descriptions() for text based descriptions of Ceteris Paribus explainers plot.ceteris_paribus_explainer works now also for categorical variables. Use theonly_numerical = FALSE to force barsingredients 0.3.12019-04-09 added references to PM VEE partial_profiles(),accumulated_profiles() andconditional_profiles for variable effectsmajor changes in function names and file names ingredients 0.3 ceteris_paribus_2d extends classical ceteris paribus profilesceteris_paribus_oscillations calculates oscilations for ceteris paribus profilesfixed examples and file names ingredients 0.2 cluster_profiles helps to identify interactionspartial_dependency calculates partial dependency plotsaggregate_profiles calculates partial dependency plots and much moreingredients 0.1 port ofmodel_feature_importance andmodel_feature_response fromDALEX toingredients added tests Contents
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