Comments (1)
What happens is that when you're splitting into training and test the factor levels are not consistent between the two. Use the resample function instead of partitioning manually, which allows you to stratify to prevent that from happening.
In general, consider using the successor mlr3 instead of mlr, which is deprecated.
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Related Issues (20)
- Error in chol.default(R) :the leading minor of order 1 is not positive definite as using the classif.naiveBayes HOT 7
- selectFeatures error HOT 2
- benchmark HOT 1
- lrn = makeLearner("regr.gausspr", importance = c("permutation")) HOT 1
- Feature names stored in `object` and `newdata` are different! using mlr package HOT 4
- `refund::pfr()` errors on R 4.1 (fgam learner) HOT 1
- createDummyFeatures gives wrong and unconsistant names used with two factor levels HOT 2
- makeClassifTask "positive" argument does not change positive class HOT 5
- Multiclass data
- [Feature request] Multilabel Feature Selection HOT 1
- How to define gamma.mu for surv.svm in paradox?
- 'surv.CoxBoost' and Integrated Learners HOT 2
- surv.glmnet and surv.cvglmnet function X matrix problem HOT 2
- `makeParamHelpList()` broken due to R help page update HOT 1
- ‘’surv.randomForestSRC” learner not found HOT 1
- Change to new cran checks badge URL HOT 1
- 'arsq' not included in Measures
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