ranger | contextual data generator used to make sensible data | Generator Utils library

This may be happening to you because some web pages loads the elements using java script, in which case the html source you receive using requests may not contain all the elements .You can check this by right-clicking on the page and selecting view source , if the data you require is in that source file you can parse it using Beautiful Soup otherwise in order to get dynamically loaded content I will suggest selenium

To be able to fiddle with the models after resampling its best to call resample with store_models = TRUE

Using your example

The method Class.forName(String className) requires a fully qualified name which means with the package. See the JavaDoc:

className - the fully qualified name of the desired class.

Notice the stacktrace saying java.lang.ClassNotFoundException: Warrior - I have noticed that there is no package included in the String. The purpose of this requirement is to avoid class ambiguity as there might be same-named classes in different packages.

Solution: You need to pass the String including the full package name, for example, pass the String "com.dt180g.project.gear.Weapon".

We recommend using DALEX for these kinds of model explainability tasks, because there is great support for tidymodels.

After you have a final fitted model (such as your random forest), you need to:

• create a DALEX explainer
• compute the PDP

This in no way a complete answer. I assume you don't want to map/create classes for each "sub class", e.g. Barbarian, Wizard etc. You could perhaps use JsonConverter. The example only handles the first "anonymous" range of objects. Maybe you'll find some of this useful.

Looks like you have things pretty well along!

As far as I know, there is no built-in function to plot a ranger tree or a randomForest tree (see here and here). However, the forest of decision trees is made up of 500 trees by default, it seems exaggerated to have a plot for each of them. There are some methods to plot decision trees from other algorithm such as rpart, party or tree. Have a look here for a brief tour of these methods for plotting trees and forests .

As per the ggplot2 documentation, you need to provide a data.frame() or object that can be converted (coerced) to a data.frame(). In this case, if you want to reproduce the plot above in ggplot2, you will need to manually set up the data frame yourself.

Below is an example of how you could set up the data to display the plot in ggplot2.

First we create a data.frame() with the variables that we want to plot. The easiest way to do this is to just group them all in as separate columns. Note that I have used the as.numeric() function to first coerce the predicted values to a vector, because they were previously a data.table row, and if you don't convert them they are maintained as rows.

you get the error because AUC is expecting a numeric vector not a factor. However, in this example, in the test set appears a new level in the column cat.column (elephant-shrew). It is good to enter all the possible values that a variable can assume both in the training and in the test set.