Prints Aggregated Profiles

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

Arguments

x

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

...

other arguments that will be passed to head()

Examples

library("DALEX") library("ingredients") model_titanic_glm <- glm(survived ~ gender + age + fare, data = titanic_imputed, family = "binomial") explain_titanic_glm <- explain(model_titanic_glm, data = titanic_imputed[,-8], y = titanic_imputed[,8])
#> Preparation of a new explainer is initiated #> -> model label : lm ( default ) #> -> data : 2207 rows 7 cols #> -> target variable : 2207 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 = 0.1490412 , mean = 0.3221568 , max = 0.9878987 #> -> residual function : difference between y and yhat ( default ) #> -> residuals : numerical, min = -0.8898433 , mean = 4.198546e-13 , max = 0.8448637 #> A new explainer has been created!
selected_passangers <- select_sample(titanic_imputed, n = 100) cp_rf <- ceteris_paribus(explain_titanic_glm, selected_passangers) head(cp_rf)
#> Top profiles : #> gender age class embarked fare sibsp parch _yhat_ #> 515 female 45 2nd Southampton 10.1000 0 0 0.6665081 #> 515.1 male 45 2nd Southampton 10.1000 0 0 0.1825295 #> 604 female 17 3rd Southampton 7.1701 1 0 0.7078368 #> 604.1 male 17 3rd Southampton 7.1701 1 0 0.2130168 #> 1430 female 25 engineering crew Southampton 0.0000 0 0 0.6838898 #> 1430.1 male 25 engineering crew Southampton 0.0000 0 0 0.1946568 #> _vname_ _ids_ _label_ #> 515 gender 515 lm #> 515.1 gender 515 lm #> 604 gender 604 lm #> 604.1 gender 604 lm #> 1430 gender 1430 lm #> 1430.1 gender 1430 lm #> #> #> Top observations: #> gender age class embarked fare sibsp parch _yhat_ #> 515 male 45 2nd Southampton 10.1000 0 0 0.1825295 #> 604 male 17 3rd Southampton 7.1701 1 0 0.2130168 #> 1430 male 25 engineering crew Southampton 0.0000 0 0 0.1946568 #> 865 male 20 3rd Cherbourg 7.0406 0 0 0.2090469 #> 452 female 17 3rd Queenstown 7.1408 0 0 0.7077927 #> 1534 male 38 victualling crew Southampton 0.0000 0 0 0.1795678 #> _label_ _ids_ #> 515 lm 1 #> 604 lm 2 #> 1430 lm 3 #> 865 lm 4 #> 452 lm 5 #> 1534 lm 6
pdp_rf <- aggregate_profiles(cp_rf, variables = "age") head(pdp_rf)
#> Top profiles : #> _vname_ _label_ _x_ _yhat_ _ids_ #> 1 age lm 0.1666667 0.3266822 0 #> 2 age lm 2.0000000 0.3241850 0 #> 3 age lm 4.0000000 0.3214750 0 #> 4 age lm 7.0000000 0.3174379 0 #> 5 age lm 9.0000000 0.3147653 0 #> 6 age lm 13.0000000 0.3094650 0
# \donttest{ library("ranger") model_titanic_rf <- ranger(survived ~., data = titanic_imputed, probability = TRUE) explain_titanic_rf <- explain(model_titanic_rf, data = titanic_imputed[,-8], y = titanic_imputed[,8], label = "ranger forest", verbose = FALSE) cp_rf <- ceteris_paribus(explain_titanic_rf, selected_passangers) cp_rf
#> Top profiles : #> gender age class embarked fare sibsp parch _yhat_ #> 515 female 45 2nd Southampton 10.1000 0 0 0.8125426 #> 515.1 male 45 2nd Southampton 10.1000 0 0 0.1098901 #> 604 female 17 3rd Southampton 7.1701 1 0 0.4618520 #> 604.1 male 17 3rd Southampton 7.1701 1 0 0.1108578 #> 1430 female 25 engineering crew Southampton 0.0000 0 0 0.7440738 #> 1430.1 male 25 engineering crew Southampton 0.0000 0 0 0.2346546 #> _vname_ _ids_ _label_ #> 515 gender 515 ranger forest #> 515.1 gender 515 ranger forest #> 604 gender 604 ranger forest #> 604.1 gender 604 ranger forest #> 1430 gender 1430 ranger forest #> 1430.1 gender 1430 ranger forest #> #> #> Top observations: #> gender age class embarked fare sibsp parch _yhat_ #> 515 male 45 2nd Southampton 10.1000 0 0 0.1098901 #> 604 male 17 3rd Southampton 7.1701 1 0 0.1108578 #> 1430 male 25 engineering crew Southampton 0.0000 0 0 0.2346546 #> 865 male 20 3rd Cherbourg 7.0406 0 0 0.1124048 #> 452 female 17 3rd Queenstown 7.1408 0 0 0.6530898 #> 1534 male 38 victualling crew Southampton 0.0000 0 0 0.1729712 #> _label_ _ids_ #> 515 ranger forest 1 #> 604 ranger forest 2 #> 1430 ranger forest 3 #> 865 ranger forest 4 #> 452 ranger forest 5 #> 1534 ranger forest 6
pdp_rf <- aggregate_profiles(cp_rf, variables = "age") head(pdp_rf)
#> Top profiles : #> _vname_ _label_ _x_ _yhat_ _ids_ #> 1 age ranger forest 0.1666667 0.5218831 0 #> 2 age ranger forest 2.0000000 0.5590009 0 #> 3 age ranger forest 4.0000000 0.5760272 0 #> 4 age ranger forest 7.0000000 0.5150417 0 #> 5 age ranger forest 9.0000000 0.4988154 0 #> 6 age ranger forest 13.0000000 0.4224053 0
# }