This is a generic predict() function works for explainer objects.
# S3 method for explainer
predict(object, newdata, ...)
model_prediction(explainer, new_data, ...)a model to be explained, object of the class explainer
data.frame or matrix - observations for prediction
other parameters that will be passed to the predict function
a model to be explained, object of the class explainer
data.frame or matrix - observations for prediction
An numeric matrix of predictions
HR_glm_model <- glm(status == "fired"~., data = HR, family = "binomial")
explainer_glm <- explain(HR_glm_model, data = HR)
#> Preparation of a new explainer is initiated
#> -> model label : lm ( default )
#> -> data : 7847 rows 6 cols
#> -> target variable : not specified! ( WARNING )
#> -> predict function : yhat.glm will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package stats , ver. 4.2.3 , task classification ( default )
#> -> model_info : Model info detected classification task but 'y' is a NULL . ( WARNING )
#> -> model_info : By deafult classification tasks supports only numercical 'y' parameter.
#> -> model_info : Consider changing to numerical vector with 0 and 1 values.
#> -> model_info : Otherwise I will not be able to calculate residuals or loss function.
#> -> predicted values : numerical, min = 0.00861694 , mean = 0.3638333 , max = 0.7822214
#> -> residual function : difference between y and yhat ( default )
#> A new explainer has been created!
predict(explainer_glm, HR[1:3,])
#> 1 2 3
#> 0.5139357 0.7384469 0.6412859
# \donttest{
library("ranger")
HR_ranger_model <- ranger(status~., data = HR, num.trees = 50, probability = TRUE)
explainer_ranger <- explain(HR_ranger_model, data = HR)
#> Preparation of a new explainer is initiated
#> -> model label : ranger ( default )
#> -> data : 7847 rows 6 cols
#> -> target variable : not specified! ( WARNING )
#> -> predict function : yhat.ranger will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package ranger , ver. 0.14.1 , task multiclass ( default )
#> -> model_info : Model info detected multiclass task but 'y' is a NULL . ( WARNING )
#> -> model_info : By deafult multiclass tasks supports only factor 'y' parameter.
#> -> model_info : Consider changing to a factor vector with true class names.
#> -> model_info : Otherwise I will not be able to calculate residuals or loss function.
#> -> predicted values : predict function returns multiple columns: 3 ( default )
#> -> residual function : difference between 1 and probability of true class ( default )
#> A new explainer has been created!
predict(explainer_ranger, HR[1:3,])
#> fired ok promoted
#> [1,] 0.7342474 0.25886552 0.006887055
#> [2,] 0.9585492 0.03760901 0.003841755
#> [3,] 0.9810689 0.01742826 0.001502793
model_prediction(explainer_ranger, HR[1:3,])
#> fired ok promoted
#> [1,] 0.7342474 0.25886552 0.006887055
#> [2,] 0.9585492 0.03760901 0.003841755
#> [3,] 0.9810689 0.01742826 0.001502793
# }