Magnus Kamau K. Lindhardt

Dear Julia, I get this following error "Error: The provided `grid` is missing the following parameter columns that have been marked for tuning by `tune()`: 'trees'.", when using the grid_latin_hypercube...

Sorry.. I found the reason: I forgot to set my ´trees = 1000´ Nw it works. However I get this error in my XGBoost tuning "Fold01, Repeat1: preprocessor 1/1, model...

Thanks for this great example. I have a question. In this example you are using XGBoost in a classification model and you naturally evaluate model performance in the end with...

Hi @julia and @JuniadMB, I also experienced the exact same error in my workflow set tuning and I don't understand why? ``` wflwset_setup

Thank you @juliasilge, I am trying to fit the individual models separately and find it difficult to interpret the issue. As the error messages are, for example here with my...

I now added an imputation step```step_impute_mean(all_predictors())``` in the recipe, and that seems to work: ``` base_recipe % step_impute_mean(all_predictors()) update_role(Latitude, Longitude, new_role = "sample ID") %>% step_zv(all_predictors(), skip = TRUE) %>%...

Thank you once again Julia for an excellent screencast! I have recently stumbled upon the DALEX package for model agnostic and exploration and I was wondering if you at the...

https://github.com/ModelOriented/DALEX

Hi Julia, Thank you for this detailed LASSO regression modelling. How would you recommend comparing model performance for an exercise where I have: 1) a linear (glmnet) model, 2) a...

Hi again Julia, Is there a way to plot the LASSO predictors vs. the actual outcome values? Like we have for glmnet linear models? Cheers,