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.
☆689Updated 2 years ago
Alternatives and similar repositories for DrWhy:
Users that are interested in DrWhy are comparing it to the libraries listed below
- moDel Agnostic Language for Exploration and eXplanation☆1,415Updated last month
- iml: interpretable machine learning R package☆496Updated last month
- 📍 Interactive Studio for Explanatory Model Analysis☆331Updated last year
- Explanatory Model Analysis. Explore, Explain and Examine Predictive Models☆186Updated 11 months ago
- H2O.ai Machine Learning Interpretability Resources☆488Updated 4 years ago
- A list of software and papers related to automatic and fast Exploratory Data Analysis☆425Updated last year
- Local Interpretable Model-Agnostic Explanations (R port of original Python package)☆484Updated 2 years ago
- Code and Resources for "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Kuhn and Johnson☆732Updated last year
- Feature Extraction And Statistics for Time Series☆300Updated 4 months ago
- Generate Diverse Counterfactual Explanations for any machine learning model.☆1,393Updated 4 months ago
- Repository with code and slides for a tutorial on causal inference.☆575Updated 5 years ago
- One day course on causal inference, MPI-EVA 9 September 2021☆244Updated 3 years ago
- Modeltime unlocks time series forecast models and machine learning in one framework☆552Updated 5 months ago
- Multivariate Imputation by Chained Equations☆462Updated this week
- Mixed Effects Random Forest☆225Updated 9 months ago
- Tidy time series forecasting☆569Updated 4 months ago
- Explaining the output of machine learning models with more accurately estimated Shapley values☆157Updated this week
- Interesting resources related to XAI (Explainable Artificial Intelligence)☆825Updated 2 years ago
- This repository contains ROC and precision-recall curve animations☆257Updated last year
- A collection of visual guides to help applied scientists learn causal inference.☆263Updated 2 years ago
- Variable Importance Plots (VIPs)☆187Updated last year
- Code for "High-Precision Model-Agnostic Explanations" paper☆799Updated 2 years ago
- An R package that makes xgboost models fully interpretable☆255Updated 6 years ago
- autoxgboost - Automatic tuning and fitting of xgboost☆122Updated 3 years ago
- R package for automation of machine learning, forecasting, model evaluation, and model interpretation☆242Updated 3 months ago
- Code and documentation for experiments in the TreeExplainer paper☆183Updated 5 years ago
- Time series analysis in the `tidyverse`☆626Updated 9 months ago
- Preliminary Exploratory Visualisation of Data☆452Updated 8 months ago
- Common statistical tests are linear models (or: how to teach stats)☆495Updated last year
- bayesplot R package for plotting Bayesian models☆437Updated last month