Hi,

I’ve noticed that xgboost does not seem to take into account the complexity of the cut when comparing a subset split criteria vs. a cut on a numerical feature.

For example, in the below, even though the target depends only on feature `a`

, xgboost chooses to cut almost entirely on feature `b`

.

```
import pandas as pd
import numpy as np
import xgboost as xgb
X = pd.DataFrame({'a': np.random.uniform(-1, 1, 10000), 'b': (list(range(5000)) * 2)})
y = X['a'] + np.random.normal(0, 5.0, 10000)
ft = ['q', 'c']
Xy = xgb.DMatrix(X, y, feature_types=ft, enable_categorical=True)
model = xgb.train({"tree_method": "hist", "max_cat_to_onehot": 5, 'base_score': 0.0, 'lambda': 5.0, 'max_cat_threshold': 5000}, Xy, num_boost_round=1)
model.trees_to_dataframe().head(20)
```

Is it possible to penalize complex cuts like these in order to reduce the likelihood of xgboost cutting on this feature?