Prints Individual Variable Explainer Summary

# S3 method for ceteris_paribus_explainer
print(x, ...)

Arguments

x

an individual variable profile explainer produced with the ceteris_paribus() function

...

other arguments that will be passed to head()

Examples

library("DALEX") library("ingredients") titanic_small <- select_sample(titanic_imputed, n = 500, seed = 1313) # build a model model_titanic_glm <- glm(survived ~ gender + age + fare, data = titanic_small, family = "binomial") explain_titanic_glm <- explain(model_titanic_glm, data = titanic_small[,-8], y = titanic_small[,8])
#> Preparation of a new explainer is initiated #> -> model label : lm ( default ) #> -> data : 500 rows 7 cols #> -> target variable : 500 values #> -> predict function : yhat.glm will be used ( default ) #> -> predicted values : No value for predict function target column. ( default ) #> -> model_info : package stats , ver. 4.1.1 , task classification ( default ) #> -> predicted values : numerical, min = 0.0795294 , mean = 0.302 , max = 0.9859411 #> -> residual function : difference between y and yhat ( default ) #> -> residuals : numerical, min = -0.8204691 , mean = 8.796651e-12 , max = 0.8567173 #> A new explainer has been created!
cp_glm <- ceteris_paribus(explain_titanic_glm, titanic_small[1,]) cp_glm
#> Top profiles : #> gender age class embarked fare sibsp parch _yhat_ _vname_ _ids_ #> 515 female 45.00 2nd Southampton 10.1 0 0 0.5595687 gender 515 #> 515.1 male 45.00 2nd Southampton 10.1 0 0 0.1448038 gender 515 #> 5151 male 0.75 2nd Southampton 10.1 0 0 0.3135247 age 515 #> 515.110 male 2.99 2nd Southampton 10.1 0 0 0.3028164 age 515 #> 515.2 male 4.98 2nd Southampton 10.1 0 0 0.2934793 age 515 #> 515.3 male 7.00 2nd Southampton 10.1 0 0 0.2841757 age 515 #> _label_ #> 515 lm #> 515.1 lm #> 5151 lm #> 515.110 lm #> 515.2 lm #> 515.3 lm #> #> #> Top observations: #> gender age class embarked fare sibsp parch _yhat_ _label_ _ids_ #> 515 male 45 2nd Southampton 10.1 0 0 0.1448038 lm 1
# \donttest{ library("ranger") apartments_rf_model <- ranger(m2.price ~., data = apartments) explainer_rf <- explain(apartments_rf_model, data = apartments_test[,-1], y = apartments_test[,1], label = "ranger forest", verbose = FALSE) apartments_small <- select_sample(apartments_test, 10) cp_rf <- ceteris_paribus(explainer_rf, apartments_small) cp_rf
#> Top profiles : #> construction.year surface floor no.rooms district _yhat_ #> 9707 1920 98 3 3 Srodmiescie 4880.584 #> 9707.1 1921 98 3 3 Srodmiescie 4890.136 #> 9707.2 1922 98 3 3 Srodmiescie 4897.864 #> 9707.3 1923 98 3 3 Srodmiescie 4866.403 #> 9707.4 1924 98 3 3 Srodmiescie 4836.051 #> 9707.5 1925 98 3 3 Srodmiescie 4833.932 #> _vname_ _ids_ _label_ #> 9707 construction.year 9707 ranger forest #> 9707.1 construction.year 9707 ranger forest #> 9707.2 construction.year 9707 ranger forest #> 9707.3 construction.year 9707 ranger forest #> 9707.4 construction.year 9707 ranger forest #> 9707.5 construction.year 9707 ranger forest #> #> #> Top observations: #> construction.year surface floor no.rooms district _yhat_ #> 9707 2008 98 3 3 Srodmiescie 4739.858 #> 9796 1932 110 10 4 Ursus 2922.596 #> 9644 1980 73 10 2 Mokotow 3703.307 #> 7567 1940 63 8 3 Praga 3271.446 #> 4090 1955 105 3 3 Ochota 3594.067 #> 8594 1999 36 9 2 Ursus 3695.560 #> _label_ _ids_ #> 9707 ranger forest 1 #> 9796 ranger forest 2 #> 9644 ranger forest 3 #> 7567 ranger forest 4 #> 4090 ranger forest 5 #> 8594 ranger forest 6
# }