I am running a classification xgboost via the mlr package. I have missing values in my data, which I would like to preserve (that is, I would like to keep these observations and I would like to avoid imputation). I understand that the xgboost implementation in mlr can handle missing values. However, I do not understand the warning provided by mlr's makeLearner function.
I have tried to read the documentation and have found this warning throughout other people's code. But I have not seen the warning addressed in a way that makes sense to me.
For example, I have read this discussion of the warning but it did not clarify things for me: https://github.com/mlr-org/mlr/pull/1225
The warning appears when calling the makeLearner function:
xgb_learner <- makeLearner(
"classif.xgboost",
predict.type = "prob",
par.vals = list(
objective = "binary:logistic",
eval_metric = "error",
nrounds = 200,
missing = NA,
max_depth = 6,
eta = 0.1,
gamma = 5,
colsample_bytree = 0.5,
min_child_weight = 1,
subsample = 0.7
)
)
Warning in makeParam(id = id, type = "numeric", learner.param = TRUE, lower = lower, :
NA used as a default value for learner parameter missing.
ParamHelpers uses NA as a special value for dependent parameters.
My missing values are currently coded as missing values (ie, NA). It is clear that R recognizes them as such from:
> sum(is.na(training$day))
[1] 58
From the getParamSet function, it seems that the parameter missing takes numeric values from -Inf to Inf. Thus, perhaps NA is not a valid value?
> getParamSet("classif.xgboost")
Warning in makeParam(id = id, type = "numeric", learner.param = TRUE, lower = lower, :
NA used as a default value for learner parameter missing.
ParamHelpers uses NA as a special value for dependent parameters.
Type len Def Constr Req Tunable Trafo
booster discrete - gbtree gbtree,gblinear,dart - TRUE -
watchlist untyped - <NULL> - - FALSE -
eta numeric - 0.3 0 to 1 - TRUE -
gamma numeric - 0 0 to Inf - TRUE -
max_depth integer - 6 1 to Inf - TRUE -
min_child_weight numeric - 1 0 to Inf - TRUE -
subsample numeric - 1 0 to 1 - TRUE -
colsample_bytree numeric - 1 0 to 1 - TRUE -
colsample_bylevel numeric - 1 0 to 1 - TRUE -
num_parallel_tree integer - 1 1 to Inf - TRUE -
lambda numeric - 1 0 to Inf - TRUE -
lambda_bias numeric - 0 0 to Inf - TRUE -
alpha numeric - 0 0 to Inf - TRUE -
objective untyped - binary:logistic - - FALSE -
eval_metric untyped - error - - FALSE -
base_score numeric - 0.5 -Inf to Inf - FALSE -
max_delta_step numeric - 0 0 to Inf - TRUE -
missing numeric - -Inf to Inf - FALSE -
Do I need to recode these as a specific value that I then pass to mlr (through missing = [specific value] in makeLearner)? Do something else? Or is this warning not a cause for concern?
Thanks so very much for any clarification.
