我想在mlr3中使用distRforest包创建一个学习器。

我的代码如下：

library(mlr3extralearners)create_learner( pkg = "." ,            classname = 'distRforest',            algorithm = '回归树',            type = 'regr',            key = 'distRforest',            package = 'distRforest',            caller = 'rpart',            feature_types = c("logical", "integer", "numeric","factor", "ordered"),            predict_types = c('response'),            properties = c("importance", "missings", "multiclass",                           "selected_features", "twoclass", "weights"),            references = FALSE,            gh_name = 'CL')

出现了以下错误：错误在sprintf(msg, …) : 参数太少

事实上，复制教程中的代码 https://mlr3book.mlr-org.com/extending-learners.html 也出现了同样的错误。

有什么想法吗？非常感谢 – c

回答：

感谢您对扩展mlr3宇宙的兴趣！有几点需要注意，首先书中的例子对我来说运行得很好，其次您的例子无法工作，因为您为regr学习器包含了classif属性。由于我无法重现您的错误，很难调试哪里出了问题，如果您能运行以下代码会很有帮助：

reprex::reprex({  create_learner(    pkg = ".",    classname = "Rpart",    algorithm = "决策树",    type = "classif",    key = "rpartddf",    package = "rpart",    caller = "rpart",    feature_types = c("logical", "integer", "numeric", "factor", "ordered"),    predict_types = c("response", "prob"),    properties = c("importance", "missings", "multiclass", "selected_features", "twoclass", "weights"),    references = TRUE,    gh_name = "CL"  )}, si = TRUE)

如果您仍然得到错误，并且输出太长无法在这里打印，那么请前往GitHub并在那里开启一个issue。