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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. (#39) WARNING: Change in the default value of baseline.yhat.* functions help to handle additional parameters to different predict() functions.HR and HRTest. Target variable is a factor with three levels. Is used in examples for classification.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)variable_response() to better support of gbm models (c8393120ffb05e2f3c70b0143c4e92dc91f6c823).plot_model_performance() (e5e61d0398459b78ea38ccc980c4040fd853f449).breakDown v 0.1.6.single_variable() / variable_response() function uses predict_function from explainer (#17)explain() function converts tibbles to data.frame when specified as data argument (#15)explain.default() should help when explain() from dplyr is loaded after DALEX (#16)model_performance(), variable_importance(), variable_response(), outlier_detection(), prediction_breakdown(). Old names are now deprecated but still working. (#12)apartments - will be used in examplesvariable_importance() allows work on full dataset if n_sample is negativeplot_model_performance() uses ecdf or boxplots (depending on geom parameter).single_variable() supports factor variables as well (with the use of factorMerger package). Remember to use type='factor' when playing with factors. (#10)explain(). Old version has an argument predict.function, now it's predict_function. New name is more consistent with other arguments. (#7)xgboost model (#11)variable_dropout() function explain() function implementedsingle_prediction() function implementedsingle_variable() function implementedAny scripts or data that you put into this service are public.
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