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DALEX (version 0.4.4)

Descriptive mAchine Learning EXplanations

Description

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 of interpretability. DALEX package contains various explainers that help to understand the link between input variables and model output. The single_variable() explainer extracts conditional response of a model as a function of a single selected variable. It is a wrapper over packages 'pdp' (Greenwell 2017) , 'ALEPlot' (Apley 2018) and 'factorMerger' (Sitko and Biecek 2017) . The single_prediction() explainer attributes parts of a model prediction to particular variables used in the model. It is a wrapper over 'breakDown' package (Staniak and Biecek 2018) . The variable_dropout() explainer calculates variable importance scores based on variable shuffling (Fisher at al. 2018) . All these explainers can be plotted with generic plot() function and compared across different models. 'DALEX' is a part of the 'DrWhy.AI' universe (Biecek 2018) .

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Version

Install

install.packages('DALEX')

Monthly Downloads

5,324

Version

0.4.4

License

GPL

Maintainer

Przemyslaw Biecek

Last Published

July 6th, 2019

Functions in DALEX (0.4.4)

predict.explainer

Wrapper over the predict function
theme_drwhy

DrWhy Theme for ggplot objects
theme_mi2

MI^2 Theme
dragons

Dragon Data
plot.variable_response_explainer

Plots Marginal Model Explanations (Single Variable Responses)
variable_importance

Feature Importance Calculated as Loss from Feature Dropout
plot.variable_importance_explainer

Plots Global Model Explanations (Variable Importance)
titanic

Passengers and Crew on the RMS Titanic
print.model_performance_explainer

Model Performance Summary
variable_response

Marginal Response for a Single Variable
print.explainer

Prints Explainer Summary
prediction_breakdown

Explanations for a Prediction Breakdown
yhat

Wrapper over the predict function
plot.prediction_breakdown_explainer

Plots Local Explanations (Single Prediction)
plot.model_performance_explainer

Model Performance Plots
plot.feature_response_explainer

Plots Marginal Model Explanations (Single Variable Responses)
HR

Human Resources Data
colors_discrete_drwhy

DrWhy color palettes for ggplot objects
explain.default

Create Model Explainer
apartments

Apartments Data
model_performance

Model Performance Plots
install_dependencies

Install all dependencies for the DALEX package
loss_cross_entropy

Preimplemented Loss Functions
feature_response

Marginal Response for a Single Feature