mlr3shiny
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mlr3shiny: Machine Learning in Shiny with mlr3
All learners will state that they support the “missings” property and can handle all data types, although this is not always the case. For example, SVM cannot handle missing values...
-Erstellen einer SVM und "Change Parameters" ausführen -wechseln des kernels und erneutes ausführen von "Change Parameters" => Shiny stürzt ab
If the created tree has no internal nodes, we receive a cryptic error message. Proposal: Plot a textual representation of the tree instead. Unfortunately, mlr3viz uses ggparty, which can't handle...
Explain tab shows only finally trained learner on all data (within predict tab). Currently, these learens can ve re-trained and evaluated (e.g. in train & evaluate tab) but then still...
If a new learner is selected in the Learner tab after another learner has previously been trained in the Predict tab, Pred$Learner remains unchanged and continues to point to the...
In the robustify pipeline, the available selection options for ordered_action are currently "factor", "factor!", "matrix", "matrix!", "ignore" and "ignore!". However, ordered factors cannot be converted into a matrix. In addition...
1.Import training data Data Type: xlsx Browse: covid_spring.xlsx 2.Task Select Data Backend: imported training data Create Task Set Positive Class: none selected
