explain: Create Model Explainer

In DALEX2: Descriptive mAchine Learning EXplanations

Description

Black-box models may have very different structures. This function creates a unified representation of a model, which can be further processed by various explainers.

Usage

explain.default(model, data = NULL, y = NULL,
  predict_function = yhat, link = I, ..., label = tail(class(model),
  1))

explain(model, data = NULL, y = NULL, predict_function = yhat,
  link = I, ..., label = tail(class(model), 1))

Arguments

model	object - a model to be explained
data	data.frame or matrix - data that was used for fitting. If not provided then will be extracted from the model
y	numeric vector with outputs / scores. Currently used only by variable_dropout() explainer.
predict_function	function that takes two arguments: model and new data and returns numeric vector with predictions
link	function - a transformation/link function that shall be applied to raw model predictions
...	other parameters
label	character - the name of the model. By default it's extracted from the 'class' attribute of the model

Details

Please NOTE, that the model is actually the only required argument. But some explainers may require that others will be provided too.

Value

An object of the class 'explainer'.

It's a list with following fields:

• model the explained model

• data the dataset used for training

• predict_function function that may be used for model predictions, shall return a single numerical value for each observation.

• class class/classes of a model

• label label, by default it's the last value from the class vector, but may be set to any character.

Examples

apartments_lm <- lm(m2.price ~ ., data = apartments)
apartments_lm_ex <- explain(apartments_lm, data = apartments, label = "apartments_lm")
apartments_lm_ex

 ## Not run: 
library("breakDown2")
wine_lm_model4 <- lm(quality ~ pH + residual.sugar + sulphates + alcohol, data = wine)
wine_lm_explainer4 <- explain(wine_lm_model4, data = wine, label = "model_4v")
wine_lm_explainer4

library("randomForest")
wine_rf_model4 <- randomForest(quality ~ pH + residual.sugar + sulphates + alcohol, data = wine)
wine_rf_explainer4 <- explain(wine_rf_model4, data = wine, label = "model_rf")
wine_rf_explainer4
 
## End(Not run)

DALEX2 documentation built on May 2, 2019, 1:19 p.m.