xgboost scores with tidymodels

[Jack564]

Hello,
After some R packages updates, the predicted probabilities of my xgboost model are very close to 0.5.
I don't know why.

My 15 predictors are numeric, and my outcome is a factor (0 or 1).
My results look like this:

.pred_0          .pred_1          .pred_class
0.5000001    0.4999999     0
0.5000002    0.4999998     0
0.4999996     0.5000004    1...

My model was selected for its best accuracy using hyperparameter tuning:

xgb_spec1 <- boost_tree(mode = "classification", 
                        mtry = tune(), 
                        trees = 1000, 
                        min_n = tune(),
                        tree_depth = tune(),
                        learn_rate = tune()) %>% set_engine("xgboost")

Is there a parameter to specify that I missed?
Thank you in advance for your help!

[Max]

It could very well be that you have no informative predictors.

You might want to post this in the modeling and machine learning group to get a faster response.

Can you please provide a minimal reprex (reproducible example)? The goal of a reprex is to make it as easy as possible for me to recreate your problem so that I can fix it: please help me help you!

If you've never heard of a reprex before, start by reading "What is a reprex", and follow the advice further down that page.