How to assess nested cross validation results in comparison to non-nested results? I have a nonlinear regression model scoring genes from scores between 0 to 1 as to whether they are likely to cause disease. Training data is ~700 gene samples by 53 features.
Currently I get results with xgboost which look like:
r2 Nested CV Average: 0.807
MSE Nested CV Average: -0.016

Non-nested Results:
XGBR Train r2: 0.949 Test r2: 0.805
XGBR Train MSE: 0.002 Test MSE: 0.018
r2: 0.895
Predicted r2: 0.871

Should I be concerned that the non-nested training result is overfitting in comparison to the test result, or should I only rely on my 5-fold nested cross-validation to determine overfitting is minimised?
For reference, the XGBoost model I use is tuned like this:
xgbr = xgboost.XGBRegressor(random_state=seed, objective='reg:squarederror') 
xgbr_params = {
    'max_depth': (1, 10),
    'learning_rate': (0.01, 0.5), 
    'n_estimators': (20, 50), 
    'reg_alpha': (1, 10),
    'reg_lambda': (1, 10),
    'gamma': (0, 0.5), 
    'min_child_weight': (1, 5),
    'subsample': (0.1, 1),
    'colsample_bytree': (0.1, 1)}

#Best parameter output:
xgbr = xgboost.XGBRegressor(random_state=seed, subsample=0.8258568992489053, min_child_weight=1, 
n_estimators=50, gamma=0.0, objective='reg:squarederror', colsample_bytree=1.0, 
learning_rate= 0.3987519903467713, max_depth=4, reg_alpha=1, reg_lambda=10)
That is not a regression problem. Thus those results can not be interpreted.
It should be framed as probabilistic binary classification. The target is binary because there are two outcomes - disease or not. Probabilistic because it on a scale from 0 (not possible) to 1 (completely certain).