moDel Agnostic Language for Exploration and eXplanation
DrWhy color palettes for ggplot objects
Data for early COVID mortality
Create Model Explainer
World Happiness Report data
Install all dependencies for the DALEX package
Calculate Loss Functions
Wrapper for Loss Functions from the yardstick Package
Dataset Level Model Diagnostics
Exract info from model
Dataset Level Variable Importance as Change in Loss Function after Var...
Dataset Level Model Performance Measures
Dataset Level Variable Profile as Partial Dependence or Accumulated Lo...
Plot List of Explanations
Plot Dataset Level Model Diagnostics
Plot Variable Importance Explanations
Plot Dataset Level Model Performance Explanations
Plot Dataset Level Model Profile Explanations
Plot Instance Level Residual Diagnostics
Plot Variable Attribution Explanations
Plot Variable Profile Explanations
Plot Generic for Break Down Objects
Predictions for the Explainer
Instance Level Residual Diagnostics
Instance Level Parts of the Model Predictions
Instance Level Profile as Ceteris Paribus
Print Natural Language Descriptions
Print Explainer Summary
Print Dataset Level Model Diagnostics
Print model_info
Print Dataset Level Model Performance Summary
Print Dataset Level Model Profile
Print Instance Level Residual Diagnostics
SHAP aggregated values
Default Theme for DALEX plots
DrWhy Theme for ggplot objects
Update data of an explainer object
Update label of explainer object
Dataset Level Variable Effect as Partial Dependency Profile or Accumul...
Wrap Various Predict Functions
Any unverified black box model is the path to failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection. DALEX package xrays any model and helps to explore and explain its behaviour. Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance. But such black-box models usually lack direct interpretability. DALEX package contains various methods that help to understand the link between input variables and model output. Implemented methods help to explore the model on the level of a single instance as well as a level of the whole dataset. All model explainers are model agnostic and can be compared across different models. DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration. Find more details in (Biecek 2018) <arXiv:1806.08915>.
Useful links