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 arts of model prediction to articular variables used in the model.
It is a wrapper over 'breakDown' package.
The variable_dropout() explainer assess variable importance based on consecutive permutations.
All these explainers can be plotted with generic plot() function and compared across different models.
Functions in DALEX
|explain||Create Model Explainer|
|plot.single_prediction_explainer||Plots Local Explanations (Single Prediction)|
|print.explainer||Prints Explainer Summary|
|single_prediction||Explanations for a Single Prediction|
|plot.single_variable_explainer||Plots Marginal Model Explanations (Single Variable Responses)|
|plot.variable_dropout_explainer||Plots Global Model Explanations (Variable Drop-out)|
|single_variable||Marginal Response for a Single Variable|
|variable_dropout||Loss from Variable Dropout|
Last month downloads
|Packaged||2018-02-28 01:44:36 UTC; pbiecek|
|Date/Publication||2018-02-28 16:36:14 UTC|
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