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| aspect_importance | Calculates the feature groups importance (called aspects importance) for a selected observation |
| aspect_importance.default | Calculates the feature groups importance (called aspects importance) for a selected observation |
| aspect_importance.explainer | Calculates the feature groups importance (called aspects importance) for a selected observation |
| aspect_importance_single | Aspects importance for single aspects |
| aspect_importance_single.default | Aspects importance for single aspects |
| aspect_importance_single.explainer | Aspects importance for single aspects |
| champion_challenger | Compare machine learning models |
| create_env | Create your conda virtual env with DALEX |
| explain_h2o | Create explainer from your h2o model |
| explain_keras | Wrapper for Python Keras Models |
| explain_mljar | Create explainer from your mljar model |
| explain_mlr | Create explainer from your mlr model |
| explain_mlr3 | Create explainer from your mlr model |
| explain_scikitlearn | Wrapper for Python Scikit-Learn Models |
| funnel_measure | Caluculate difference in performance in models across different categories |
| get_sample | Function for getting binary matrix |
| group_variables | Groups numeric features into aspects |
| lime | Calculates the feature groups importance (called aspects importance) for a selected observation |
| model_info.H2OBinomialModel | Exract info from model |
| model_info.H2ORegressionModel | Exract info from model |
| model_info.keras | Exract info from model |
| model_info.LearnerClassif | Exract info from model |
| model_info.LearnerRegr | Exract info from model |
| model_info.mljar_model | Exract info from model |
| model_info.scikitlearn_model | Exract info from model |
| model_info.WrappedModel | Exract info from model |
| overall_comparison | Compare champion with challengers globally |
| plot.aspect_importance | Function for plotting aspect_importance results |
| plot.funnel_measure | Funnel plot for difference in measures |
| plot.overall_comparison | Plot function for overall_comparison |
| plot.training_test_comparison | Plot and compare performance of model between training and test set |
| plot_aspects_importance_grouping | Function plots tree with aspect importance values |
| plot_group_variables | Plots tree with correlation values |
| print.funnel_measure | Print funnel_measure object |
| print.overall_comparison | Print overall_comparison object |
| print.scikitlearn_set | Prints scikitlearn_set class |
| print.training_test_comparison | Print funnel_measure object |
| training_test_comparison | Compare performance of model between training and test set |
| triplot | Three plots that sum up automatic aspect importance grouping |
| triplot.default | Three plots that sum up automatic aspect importance grouping |
| triplot.explainer | Three plots that sum up automatic aspect importance grouping |
| yhat.H2OBinomialModel | Wrapper over the predict function |
| yhat.H2ORegressionModel | Wrapper over the predict function |
| yhat.keras | Wrapper over the predict function |
| yhat.LearnerClassif | Wrapper over the predict function |
| yhat.LearnerRegr | Wrapper over the predict function |
| yhat.mljar_model | Wrapper over the predict function |
| yhat.scikitlearn_model | Wrapper over the predict function |
| yhat.WrappedModel | Wrapper over the predict function |