This is a generic predict() function works for explainer objects.

# S3 method for explainer
predict(object, newdata, ...)

model_prediction(explainer, new_data, ...)

Arguments

object

a model to be explained, object of the class explainer

newdata

data.frame or matrix - observations for prediction

...

other parameters that will be passed to the predict function

explainer

a model to be explained, object of the class explainer

new_data

data.frame or matrix - observations for prediction

Value

An numeric matrix of predictions

Examples

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. 4.1.1 , 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. 0.13.1 , 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,] 0.7392260 0.2549723 0.005801643 #> [2,] 0.9556583 0.0404699 0.003871795 #> [3,] 0.9702200 0.0242534 0.005526593
model_prediction(explainer_ranger, HR[1:3,])
#> fired ok promoted #> [1,] 0.7392260 0.2549723 0.005801643 #> [2,] 0.9556583 0.0404699 0.003871795 #> [3,] 0.9702200 0.0242534 0.005526593
# }