Package: DALEXtra 2.3.0
Szymon Maksymiuk
DALEXtra: Extension for 'DALEX' Package
Provides wrapper of various machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the interpretable machine learning, there are more and more new ideas for explaining black-box models, that are implemented in 'R'. 'DALEXtra' creates 'DALEX' Biecek (2018) <arxiv:1806.08915> explainer for many type of models including those created using 'python' 'scikit-learn' and 'keras' libraries, and 'java' 'h2o' library. Important part of the package is Champion-Challenger analysis and innovative approach to model performance across subsets of test data presented in Funnel Plot.
Authors:
DALEXtra_2.3.0.tar.gz
DALEXtra_2.3.0.zip(r-4.5)DALEXtra_2.3.0.zip(r-4.4)DALEXtra_2.3.0.zip(r-4.3)
DALEXtra_2.3.0.tgz(r-4.4-any)DALEXtra_2.3.0.tgz(r-4.3-any)
DALEXtra_2.3.0.tar.gz(r-4.5-noble)DALEXtra_2.3.0.tar.gz(r-4.4-noble)
DALEXtra_2.3.0.tgz(r-4.4-emscripten)DALEXtra_2.3.0.tgz(r-4.3-emscripten)
DALEXtra.pdf |DALEXtra.html✨
DALEXtra/json (API)
NEWS
| # Install 'DALEXtra' in R: |
| install.packages('DALEXtra', repos = c('https://modeloriented.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/modeloriented/dalextra/issues
Last updated 1 years agofrom:a8baf5791b. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 15 2024 |
| R-4.5-win | OK | Nov 15 2024 |
| R-4.5-linux | OK | Nov 15 2024 |
| R-4.4-win | OK | Nov 15 2024 |
| R-4.4-mac | OK | Nov 15 2024 |
| R-4.3-win | OK | Nov 15 2024 |
| R-4.3-mac | OK | Nov 15 2024 |
Exports:champion_challengercreate_envdalex_load_explainerexplain_h2oexplain_kerasexplain_mlrexplain_mlr3explain_scikitlearnexplain_tidymodelsexplain_xgboostfunnel_measuremodel_type.dalex_explaineroverall_comparisonpredict_model.dalex_explainerpredict_surrogatepredict_surrogate_imlpredict_surrogate_limepredict_surrogate_local_modeltraining_test_comparison
Dependencies:clicodetoolscolorspaceDALEXfansifarverforeachggplot2gluegridExtragtableiBreakDowningredientsisobanditeratorskernelshaplabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compare machine learning models | champion_challenger |
| Create your conda virtual env with DALEX | create_env |
| DALEX load explainer | dalex_load_explainer |
| Create explainer from your h2o model | explain_h2o |
| Wrapper for Python Keras Models | explain_keras |
| Create explainer from your mlr model | explain_mlr |
| Create explainer from your mlr model | explain_mlr3 |
| Wrapper for Python Scikit-Learn Models | explain_scikitlearn |
| Create explainer from your tidymodels workflow. | explain_tidymodels |
| Create explainer from your xgboost model | explain_xgboost |
| Caluculate difference in performance in models across different categories | funnel_measure |
| Exract info from model | model_info.GraphLearner model_info.H2OBinomialModel model_info.H2OMultinomialModel model_info.H2ORegressionModel model_info.keras model_info.LearnerClassif model_info.LearnerRegr model_info.model_stack model_info.scikitlearn_model model_info.workflow model_info.WrappedModel model_info.xgb.Booster |
| Compare champion with challengers globally | overall_comparison |
| Funnel plot for difference in measures | plot.funnel_measure |
| Plot function for overall_comparison | plot.overall_comparison |
| Plot and compare performance of model between training and test set | plot.training_test_comparison |
| Instance Level Surrogate Models | model_type.dalex_explainer plot.predict_surrogate_lime predict_model.dalex_explainer predict_parts predict_parts_break_down predict_parts_ibreak_down predict_parts_shap predict_surrogate predict_surrogate_iml predict_surrogate_lime predict_surrogate_local_model |
| Print funnel_measure object | print.funnel_measure |
| Print overall_comparison object | print.overall_comparison |
| Prints scikitlearn_set class | print.scikitlearn_set |
| Print funnel_measure object | print.training_test_comparison |
| Compare performance of model between training and test set | training_test_comparison |
| Wrapper over the predict function | yhat.GraphLearner yhat.H2OBinomialModel yhat.H2OMultinomialModel yhat.H2ORegressionModel yhat.keras yhat.LearnerClassif yhat.LearnerRegr yhat.model_stack yhat.scikitlearn_model yhat.workflow yhat.WrappedModel yhat.xgb.Booster |
