mlr3 - confidence interval for predictions

1
votes

After tuning a learner and using it, we can use it to make predictions through the command line

predict(Learner, newdata, predict_type="response")

But, how do we compute confidence intervals for predictions?

task <- TaskRegr$new("data", data, "y")
learner <- lrn("regr.xgboost")
preprocess <- po("scale", param_vals = list(center = TRUE, scale = TRUE))
pp <- preprocess %>>% learner
gg<- GraphLearner$new(pp)
gg$train(task)
predict(gg, newdata = pred, predict_type="reponse")
1
rmlr3
predict_type = "se" – Lars Kotthoff
It throws an error --- Error: Predict type 'se' not available. I'm using regr.xgboost in a GraphLearner. – Nip
Can you post a complete example that allows to reproduce this please? You probably just have to adjust how the model is trained. – Lars Kotthoff
When I use lrn("regr.featureless", predict_type = "se") it shows not errors at all. lrn("regr.xgboost", predict_type = "se") throws Error: Learner 'regr.xgboost' does not support predict type 'se', however. Is it just me? – Nip
No, not all learners support prediction errors, xgboost being one of them. You'll have to use a different learner to get error estimates. – Lars Kotthoff

1 Answers

4
votes

Not all learners support prediction errors, xgboost being one of them. You'll have to use a different learner to get error estimates.