在 mlr3 中创建学习者:sprintf(msg, ...) 错误:参数太少
creating learner in mlr3: Error in sprintf(msg, ...) : too few arguments
我想在 mlr3 中创建一个学习者,使用 distRforest 包。
我的代码:
library(mlr3extralearners)
create_learner( pkg = "." ,
classname = 'distRforest',
algorithm = 'regression tree',
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'
)
给出以下错误:sprintf(msg, ...) 错误:参数太少
事实上,复制教程中的代码 https://mlr3book.mlr-org.com/extending-learners.html 会引发相同的错误。
有什么想法吗?非常感谢 - c
感谢您对扩展 mlr3 宇宙的兴趣!
有几件事,首先,书中的示例对我来说很好用,其次,您的示例不起作用,因为您包含 classif
学习者的 classif
属性。由于我无法重现您的错误,因此我很难调试出了什么问题,如果您可以 运行 以下内容将会很有帮助:
reprex::reprex({
create_learner(
pkg = ".",
classname = "Rpart",
algorithm = "decision tree",
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)
如果您仍然遇到错误并且输出太长而无法在此处打印,请转到 GitHub 并在那里打开一个问题。
我想在 mlr3 中创建一个学习者,使用 distRforest 包。
我的代码:
library(mlr3extralearners)
create_learner( pkg = "." ,
classname = 'distRforest',
algorithm = 'regression tree',
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'
)
给出以下错误:sprintf(msg, ...) 错误:参数太少
事实上,复制教程中的代码 https://mlr3book.mlr-org.com/extending-learners.html 会引发相同的错误。
有什么想法吗?非常感谢 - c
感谢您对扩展 mlr3 宇宙的兴趣!
有几件事,首先,书中的示例对我来说很好用,其次,您的示例不起作用,因为您包含 classif
学习者的 classif
属性。由于我无法重现您的错误,因此我很难调试出了什么问题,如果您可以 运行 以下内容将会很有帮助:
reprex::reprex({
create_learner(
pkg = ".",
classname = "Rpart",
algorithm = "decision tree",
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)
如果您仍然遇到错误并且输出太长而无法在此处打印,请转到 GitHub 并在那里打开一个问题。