[Python] Scoring in GridSearchCV for XGBoost

I'm currently trying to analyze data for the first time using XGBoost. I want to find the best parameters using GridsearchCV. I want to minimize the root mean squared error and to do this, I used "rmse" as eval_metric. However, scoring in grid search does not have such a metric. I found on this site that the "neg_mean_squared_error" does the same, but I found that this gives me different results than the RMSE. When I calculate the root of the absolute value of the "neg_mean_squared_error", I get a value of around 8.9 while a different function gives me a RMSE of about 4.4. I don't know what goes wrong or how I get these two functions to agree/give the same values?

Because of this problem, I get wrong values as "best_params_" which give me a higher RMSE than some values I initially started with to tune.

Can anyone please explain me how to get score on the RMSE in the grid search or why my code gives different values?

Thanks in advance.

def modelfit(alg, trainx, trainy, useTrainCV=True, cv_folds=10, early_stopping_rounds=50):
if useTrainCV:
xgb_param = alg.get_xgb_params()
xgtrain = xgb.DMatrix(trainx, label=trainy)
cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,
metrics='rmse', early_stopping_rounds=early_stopping_rounds)
alg.set_params(n_estimators=cvresult.shape[0])

# Fit the algorithm on the data
alg.fit(trainx, trainy, eval_metric='rmse')

# Predict training set:
dtrain_predictions = alg.predict(trainx)
# dtrain_predprob = alg.predict_proba(trainy)[:, 1]
print(dtrain_predictions)
print(np.sqrt(mean_squared_error(trainy, dtrain_predictions)))

# Print model report:
print("\nModel Report")
print("RMSE : %.4g" % np.sqrt(metrics.mean_squared_error(trainy, dtrain_predictions)))

param_test2 = {
'max_depth':[6,7,8],
'min_child_weight':[2,3,4]
}

grid2 = GridSearchCV(estimator = xgb.XGBRegressor( learning_rate =0.1, n_estimators=2000, max_depth=5,
min_child_weight=2, gamma=0, subsample=0.8, colsample_bytree=0.8,
objective= 'reg:linear', nthread=4, scale_pos_weight=1, random_state=4),
param_grid = param_test2, scoring='neg_mean_squared_error', n_jobs=4,iid=False, cv=10, verbose=20)
grid2.fit(X_train,y_train)
# Mean cross-validated score of the best_estimator
print(grid2.best_params_, np.sqrt(np.abs(grid2.best_score_))), print(np.sqrt(np.abs(grid2.score(X_train, y_train))))
modelfit(grid2.best_estimator_, X_train, y_train)
print(np.sqrt(np.abs(grid2.score(X_train, y_train))))

Continue reading...