Descriptive mAchine Learning EXplanations
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.
Functions in DALEX
|print.model_performance_explainer||Model Performance Summary|
|plot.prediction_breakdown_explainer||Plots Local Explanations (Single Prediction)|
|variable_importance||Loss from Variable Dropout|
|variable_response||Marginal Response for a Single Variable|
|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|
Last month downloads
|Packaged||2018-08-05 22:59:31 UTC; pbiecek|
|Date/Publication||2018-08-06 06:10:03 UTC|
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