ModelOriented / DrWhy
DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.
☆681Updated last year
Related projects ⓘ
Alternatives and complementary repositories for DrWhy
- moDel Agnostic Language for Exploration and eXplanation☆1,373Updated last month
- iml: interpretable machine learning R package☆492Updated 3 weeks ago
- 📍 Interactive Studio for Explanatory Model Analysis☆326Updated last year
- Explanatory Model Analysis. Explore, Explain and Examine Predictive Models☆181Updated 6 months ago
- A list of software and papers related to automatic and fast Exploratory Data Analysis☆422Updated 8 months ago
- Local Interpretable Model-Agnostic Explanations (R port of original Python package)☆485Updated 2 years ago
- H2O.ai Machine Learning Interpretability Resources☆483Updated 3 years ago
- Code and Resources for "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Kuhn and Johnson☆721Updated last year
- An R package that makes xgboost models fully interpretable☆252Updated 6 years ago
- Modeltime unlocks time series forecast models and machine learning in one framework☆534Updated 2 weeks ago
- Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, …☆673Updated 4 months ago
- Variable Importance Plots (VIPs)☆186Updated last year
- Mixed Effects Random Forest☆219Updated 4 months ago
- A collection of visual guides to help applied scientists learn causal inference.☆245Updated 2 years ago
- Survival analysis built on top of scikit-learn☆1,134Updated this week
- Feature Extraction And Statistics for Time Series☆295Updated this week
- A set of tools to understand what is happening inside a Random Forest☆230Updated 7 months ago
- Multivariate Imputation by Chained Equations☆445Updated 2 weeks ago
- Flexible tool for bias detection, visualization, and mitigation☆86Updated 2 years ago
- Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).☆1,396Updated this week
- Combining tree-boosting with Gaussian process and mixed effects models☆567Updated this week
- Code and documentation for experiments in the TreeExplainer paper☆179Updated 5 years ago
- autoxgboost - Automatic tuning and fitting of xgboost☆121Updated 2 years ago
- Tidy anomaly detection☆339Updated 10 months ago
- R package for automation of machine learning, forecasting, model evaluation, and model interpretation☆236Updated 2 months ago
- Tidy time series forecasting☆564Updated this week
- Explaining the output of machine learning models with more accurately estimated Shapley values☆145Updated last week
- Time series analysis in the `tidyverse`☆614Updated 4 months ago
- Improving XGBoost survival analysis with embeddings and debiased estimators☆321Updated last month
- One day course on causal inference, MPI-EVA 9 September 2021☆245Updated 2 years ago