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DALEX (version 0.2.4)

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' and 'ALEPlot'. The single_prediction() explainer attributes parts of a model prediction to particular variables used in the model. It is a wrapper over 'breakDown' package. The variable_dropout() explainer calculates variable importance scores based on variable shuffling. All these explainers can be plotted with generic plot() function and compared across different models.

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Version

Install

install.packages('DALEX')

Monthly Downloads

5,324

Version

0.2.4

License

GPL

Maintainer

Przemyslaw Biecek

Last Published

August 6th, 2018

Functions in DALEX (0.2.4)

print.model_performance_explainer

Model Performance Summary
plot.prediction_breakdown_explainer

Plots Local Explanations (Single Prediction)
theme_mi2

MI^2 Theme
variable_importance

Loss from Variable Dropout
variable_response

Marginal Response for a Single Variable
apartments

Apartments Data
plot.variable_importance_explainer

Plots Global Model Explanations (Variable Importance)
model_performance

Model Performance Plots
explain.default

Create Model Explainer
print.explainer

Prints Explainer Summary
prediction_breakdown

Explanations for a Single Prediction
HR

Human Resources Data
plot.variable_response_explainer

Plots Marginal Model Explanations (Single Variable Responses)
plot.model_performance_explainer

Model Performance Plots