text_plain_output_1.png
from sklearn.metrics import mean_squared_log_error

from xgboost import XGBRegressor

from xgboost import XGBRegressor
cat1 = XGBRegressor(n_estimators=500, learning_rate=0.1, early_stopping_rounds=5, max_depth=3).fit(X_train, y_train)
y_cat = cat1.predict(X_test)
mean_squared_log_error(y_test, y_cat)