Comments (10)
for classif: i would always set the predict type to "prob", i dont think there is a downside to this
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what is harder is how we let the user select the params
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IMHO the learners should be selected on their own page?
they already seem to influence multiple things?
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IMHO we have three options
- Let the user enter the par.vals list directly
- Have some hand coded interfaces for the most popular learners
- Parse the par.set of the Learner and generate a shiny UI based on that. For every param type there will be a specific sidebarPanel and so on. Tedious but doable
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how about we have a textbox per selected learner, where the user enters the parvals, simply as an expression. thats simple, but flexible. but while that learner is in focus / selected, we also display its param set. so i can see how stuff is named / called.
later on the UI could be improved.
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there are some learners (e.g. knn, lvq1 and rferns) that do not have the property "prob".
On the three options: I would prefer option 3. Maybe the interface for the parameters could be created automatically after setting an algorithm (e.g. with help of getParamSet). I do not know if this is possible in shiny.
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On the three options: I would prefer option 3. Maybe the interface for the parameters could be created automatically after setting an algorithm (e.g. with help of getParamSet). I do not know if this is possible in shiny.
of course one can program this, also with shiny. but it will be work. i would suggest to do the simpler thing first....
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Since #24 we have a learner tab, where all learners for the ongoing steps are selected and constructed.
For each learner the user can see the ParamSet and then enter the par.vals as raw text.
If a classification learner supports probs the user can select the predict type.
Next steps:
- implement sliders and buttons for the different param types
- Allow predict.type = "se" for regression learners
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just found a small bug: when changing a param of a learner in the learner section, automatically the tab of the first learner is selected again.
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this issue seems to be obsolete since PR #63
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Related Issues (20)
- better output for calculateConfusionMatrix HOT 1
- Optimize current state of app
- Improve plotly plots HOT 1
- Automatic less-file compiling
- Loading screen HOT 1
- R-package shinyMlr HOT 7
- Info: Repo renamed HOT 1
- Setup Travis HOT 1
- Remove branch package
- We need unit tests
- Error when starting the app HOT 8
- Error in opening. HOT 15
- prediction plots have wrong size HOT 1
- ERROR: could not find function "sidebarMenu" with shinyMLR in R 3.4.1 HOT 3
- Add notes about Mac installation HOT 1
- What's the maximum file size that can be uploaded? HOT 3
- There should be a download button to export trained models
- test fails
- Package just for explorative analysis
- Error when running the app using the runApp('App.R')
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