# DALEX v0.1.1

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## 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

 Name Description 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 theme_mi2 MI^2 Theme variable_dropout Loss from Variable Dropout No Results!