DALEX 1.0

• variable_profile calls ingredients::ceteris_paribus (#131).
• variable_response and feature_response moved to variable_effect and now it calls ingredients::partial_dependency (#131).
• prediction_breakdown moved to variable_attribution and now it calls iBreakDown::break_down (#131).
• updated variable_importance, not it calls the ingredients::variable_importance (#131).
• updated model_performance (#130).
• added yhat for lrm models from rms package
• theme_drwhy has now left aligned title and subtitle.
• residuals_distribution calculates now diagnostic plots based on residuals (#143).
• model_performance calculates several metrics for classification and regression models (#146).
• plot.model_performance now supports ROC charts, LIFT charts, Cummulative Gain charts, histograms, boxplots and ecdf
• plot.model_performance now supports ROC charts, LIFT charts, Cummulative Gain charts, histograms, boxplots and ecdf
• residuals_distributon is now individual_diagnostics and produces objects of the class individual_diagnostics_explainers
• plot.individual_diagnostics_explainers now plots objects of the class individual_diagnostics_explainers
• yhat for caret models now returns matrix instead of data.frame
• model_diagnostics new function that plots residuals againes selected variable
• names of functions are changed to be compliant with latest version of the XAI pyramide

DALEX 0.4.9

• updated titanic_imputed (#113).
• added weights to the explainer. Note that not all explanations know how to handle weights (#118).
• yhat() and model_info() now support models created with gbm package.

DALEX 0.4.8

• new argument colorize in the explain() as requested in (#112).
• new generic function model_info(). It will extract basic irnformation like model package nam version and task type. (#109, #110)
• new functions update_data() and update_label(). (#114))

DALEX 0.4.7

• new dataset titanic_imputed as requested in (#104).
• the explain() function now detects if target variable y is present in the data as requested in (#103).
• the DALEX GitHub repository is transfered from pbiecek/DALEX to ModelOriented/DALEX.

DALEX 0.4.6

• Examples updated. Now they use only datasets available from DALEX.
• yhat.H2ORegressionModel and yhat.H2OBinomialModel moved to (DALEXtra) and merged into explain_h2o() function.
• yhat.WrappedModelmoved to (DALEXtra) and merged as explain_mlr() function.
• Wrapper for scikit-learn models restored in (DALEXtra) package.
• loss_one_minus_auc function added to loss_functions.R. It uses 1-auc to compute loss. Function created by Alicja Gosiewska.
• Extension for DALEX avaiable at (DALEXtra)

DALEX 0.4.5

• the explain() function is more verbose. With verbose = TRUE (default) it prints detailed information about elements of an explainer (#95).

DALEX 0.4.4

• new color schemes: colors_breakdown_drwhy(), colors_discrete_drwhy() and colors_diverging_drwhy().
• in this version the scikitlearn_model() is removed as it is not working with python 2.7

DALEX 0.4.3

• New support for scikit-learn models via scikitlearn_model()

DALEX 0.4.2

• New yhat functions for mlr, h2o and caret packages (added by Szymon).

DALEX 0.4.1

• plot.variable_importance_explainer() has now desc_sorting argument. If FALSE then variable importance will be sorted in an increasing order (#41).

DALEX 0.4.0

• ingredients and iBreakDown are added to additional features (#72).
• feature_response() and variable_response() are marked as Deprecated. It is suggested to use ingredients::partial_dependency(), ingredients::accumulated_dependency() instead (#74).
• variable_importance() is marked as Deprecated. It is suggested to use ingredients::feature_importance() instead (#75).
• prediction_breakdown() is marked as Deprecated. It is suggested to use iBreakDown::break_down() or iBreakDown::shap() instead (#76).

DALEX 0.3.1

• updated filenames

DALEX 0.3

• pdp, factorMerger and ALEPlot are going to Suggested. (#60). In next releases they will be deprecated.
• added predict function that calls the predict_function hidden in the explainer object. (#58).

DALEX 0.2.9

• the titanic dataset is copied from stablelearner package. Some features are transformed (some NA replaced with 0, more numeric features).

DALEX 0.2.8

• DALEX is being prepared for tighter integration with iBreakDown and ingredients.
• temporally there is a duplicated single_variable and single_feature
• Added new theme_drwhy().
• New arguments in the plot.variable_importance_explainer(). Namely bar_width with widths of bars and show_baseline if baseline shall be included in these plots.
• New skin in the plot.variable_response_explainer().
• New skin in the plot.prediction_breakdown_explainer().

DALEX 0.2.7

• Test datasets are now named apartments_test and HR_test
• For binary classification we return just a second column. NOTE: this may cause some unexpected problems with code dependend on defaults for DALEX 0.2.6.

DALEX 0.2.6

• New versions of yhat for ranger and svm models.

DALEX 0.2.5

• Residual distribution plots for model performance are now more legible when multiple models are plotted. The styling of plot and axis titles have also been improved (@kevinykuo).
• The defaults of single_prediction() are now consistent with breakDown::broken(). Specifically, baseline is now 0 by default instead of "Intercept". The user can also specify the baseline and other arguments by passing them to single_prediction (@kevinykuo, #39). WARNING: Change in the default value of baseline.
• New yhat.* functions help to handle additional parameters to different predict() functions.
• Updated CITATION info

DALEX 0.2.4

• New dataset HR and HRTest. Target variable is a factor with three levels. Is used in examples for classification.
• The plot.model_performance() has now show_outliers parameter. Set it to anything >0 and observations with largest residuals will be presented in the plot. (#34)

DALEX 0.2.3

• Small fixes in variable_response() to better support of gbm models (c8393120ffb05e2f3c70b0143c4e92dc91f6c823).
• Better title for plot_model_performance() (e5e61d0398459b78ea38ccc980c4040fd853f449).
• Tested with breakDown v 0.1.6.

DALEX 0.2.2

• The single_variable() / variable_response() function uses predict_function from explainer (#17)

DALEX 0.2.1

• The explain() function converts tibbles to data.frame when specified as data argument (#15)
• The default generic explain.default() should help when explain() from dplyr is loaded after DALEX (#16)

DALEX 0.2.0

• New names for some functions: model_performance(), variable_importance(), variable_response(), outlier_detection(), prediction_breakdown(). Old names are now deprecated but still working. (#12)
• A new dataset apartments - will be used in examples
• variable_importance() allows work on full dataset if n_sample is negative
• plot_model_performance() uses ecdf or boxplots (depending on geom parameter).

DALEX 0.1.8

• Function single_variable() supports factor variables as well (with the use of factorMerger package). Remember to use type='factor' when playing with factors. (#10)
• Change in the function explain(). Old version has an argument predict.function, now it’s predict_function. New name is more consistent with other arguments. (#7)
• New vigniette for xgboost model (#11)

DALEX 0.1.1

• Support for global model structure explainers with variable_dropout() function

DALEX 0.1

• DALEX package is now public
• explain() function implemented
• single_prediction() function implemented
• single_variable() function implemented