ModelOriented / DALEX-docsLinks
Documentation for the DALEX project
☆36Updated last year
Alternatives and similar repositories for DALEX-docs
Users that are interested in DALEX-docs are comparing it to the libraries listed below
Sorting:
- Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)☆84Updated last year
- Model verification, validation, and error analysis☆58Updated last year
- Structure mining for xgboost model☆26Updated 4 years ago
- An R wrapper of SHAP python library☆59Updated 2 years ago
- Model Agnostics breakDown plots☆103Updated last year
- Forecasting with H2O AutoML. Use the H2O Automatic Machine Learning algorithm as a backend for Modeltime Time Series Forecasting.☆44Updated last year
- Extensions for the DALEX package☆67Updated 10 months ago
- stray {Search and TRace AnomalY}. Full paper is available from https://arxiv.org/pdf/1908.04000.pdf☆58Updated last year
- Package for a nice and smoothe usage of the shapley value for mlr☆26Updated 6 years ago
- Machine learning pipelines for R.☆67Updated 8 years ago
- An R interface to the Python module Featuretools☆50Updated 5 years ago
- An R package to assess feature importance☆33Updated 4 years ago
- recommendations for creating R modeling packages☆41Updated 3 years ago
- modelDown generates a website with HTML summaries for predictive models☆120Updated 2 years ago
- ☆26Updated 7 years ago
- Feature-based Forecast Model Selection (FFORMS)☆78Updated 2 years ago
- R package to tune parameters for machine learning(Support Vector Machine, Random Forest, and Xgboost), using bayesian optimization with g…☆48Updated 5 years ago
- Effects and Importances of Model Ingredients☆37Updated 2 years ago
- autoxgboost - Automatic tuning and fitting of xgboost☆123Updated 3 years ago
- eXtreme RuleFit (sparse linear models on XGBoost ensembles)☆43Updated 2 years ago
- Composable Preprocessing Operators for MLR☆37Updated last week
- Adaptive and automatic gradient boosting computations.☆68Updated 2 years ago
- Hierarchical and Grouped Time Series☆111Updated 6 months ago
- A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.☆95Updated 2 years ago
- Automated Feature Selection☆65Updated 6 years ago
- Explain black box with GLM☆23Updated 5 years ago
- Flexible tool for bias detection, visualization, and mitigation☆86Updated 4 months ago
- Fast approximate Shapley values in R☆123Updated last month
- fable extension for the prophet forecasting procedure☆56Updated last year
- Variable Importance Plots (VIPs)☆187Updated last year