Rajeev Jain
Rajeev Jain
@jakob-r Thanks! But "Your initial design has to contain each discrete value at least once so that the surrogate can make predictions." is not sufficient if I use the learner...
Even if I increase the propose.points to 1000, I get the error: Error in predict.randomForest(getLearnerModel(x), newdata = .newdata, : New factor levels not present in the training data for this...
```r changing surr.rf = makeLearner("regr.randomForest", predict.type = "se", fix.factors.prediction = TRUE, se.method = "bootstrap", se.boot = 8) ``` to ```r surr.rf = makeLearner("regr.randomForest", predict.type = "se", fix.factors.prediction = TRUE, )...
just found that changing the se.method = "bootstrap", to se.method = "jackknife", works.
https://stackoverflow.com/questions/50056356/could-not-interpret-optimizer-identifier-error-in-keras
> Is there anything in the performance callback that is Intel-specific? I may lift it into candle_lib unless there are objections (and after testing) tested a few and it seems...
Gotcha. Not all .txt files were changed. I was using `uno_auc_model.txt` in my fork. https://github.com/rajeeja/Benchmarks/tree/AUCstd1
Is `--config_file ` broken?
@jmohdyusof @j-woz please comment for changes on this.
maybe we can get rid of the notebooks.