Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance, but such black-box models usually lack of interpretability. DALEX package contains various explainers that help to understand the link between input variables and model output. The single_variable() explainer extracts conditional response of a model as a function of a single selected variable. It is a wrapper over packages 'pdp' and 'ALEPlot'. The single_prediction() explainer attributes parts of a model prediction to particular variables used in the model. It is a wrapper over 'breakDown' package. The variable_dropout() explainer calculates variable importance scores based on variable shuffling. All these explainers can be plotted with generic plot() function and compared across different models.
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() functionexplain() function implementedsingle_prediction() function implementedsingle_variable() function implemented