# DALEX v0.2.4

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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 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

 Name Description 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 No Results!