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The neural net doesn't have a parameter called mixture, and the regularized regression model doesn't have parameters called hidden_units or epochs. You can't use the same grid of parameters for both of the models because they don't have the same hyperparameters. Instead, you will want to:

• create separate grids for the two models
• use option_add() to add each grid to its model via the id argument

Also check out Chapter 15 of TMwR to see more about how to add an option to only a specific workflow. Since you are using a Latin hybercube, which is the default in tidymodels, you might want to just skip all that and use grid = 30 instead.

I checked your code and I see, when looping, you added empty href attributes in every element in that dropdown menu, basically, you are telling them that when that element is clicked, refresh the page please.

el.setAttribute("href", "");

Remove that line, and you got yourself a fix to your problem.

Does this generate the output you expect?

Initialise the RequestFormList as part of its declaration then it will never be null:

public List RequestFormList { get;set;} = new List();

In Scilab leastsq (based on optim) is very poor and doesn't have global convergence properties, unlike ipopt which is available as an atoms module. Install it like this:

the second chunk in your question works fine when I attach the package named tune. I think it's a better way to attach tidymodels family to your workspace via library(tidymodels) wrapper rather than attaching individually.

If tidymodels package installed correctly, (run a <- require(tidymodels) and a should be logical TRUE) this piece of code will work;

I think it should be along the lines of

You are on the right track. step_naomit() is unfortunately not the answer, the error arises in step_dummy() because it contains missing data and it doesn't know how to deal with it. The solution is to use step_unknown() right before, it will take a factor variable and assign "unknown" to missing values.

I would also recommend that you do not set skip = T in step_center() and step_scale() as it would apply centering and scaling when fitting the model but would skip if when the model is used later on, such as in prediction. This would strange and undesired results.

you can get directly from Soup or your List[0].Python has find method for strings and bs4 has find method. your for loop will give you an error TypeError: find() takes no keyword.