How to train a XGBoost in how I believe is the best way (on large data)

xgboost_train.py

import xgboost as xgb

# Notice the large number of trees and the low learning rate.
# There are other important parameters like `subsample`, `min_child_weight` `colsample_bytree` but I'll leave that up
#   to you and grid searching.
gbm = xgb.XGBRFRegressor(n_estimators=10000, learning_rate=0.01, n_jobs=-1)

# Training with automatic termination
gbm.fit(
    x_train, y_train,
    eval_set=[(x_val, y_val)],
    eval_metric='rmse',
    early_stopping_rounds=50,
    verbose=True
)

# Predicting with the model that did the best on the test set
gbm.predict(x_test, ntree_limit=gbm.best_ntree_limit)