DMatrix in MultiOutput XGBoost Regressor

Question

I am trying to convert a hyperparameter tuning algorithm to a MultiOutput regression setup, can someone please help me create DMatrix for the same. Here is the code for reference:

def modelfit(alg, dtrain, predictors, useTrainCV=True, cv_folds=5, early_stopping_rounds=50):

if useTrainCV:
    xgb_param = alg.get_xgb_params()
    xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values)
    cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,
        metrics='auc', early_stopping_rounds=early_stopping_rounds, show_progress=False)
    alg.set_params(n_estimators=cvresult.shape[0])

#Fit the algorithm on the data
alg.fit(dtrain[predictors], dtrain['Disbursed'],eval_metric='auc')
    
#Predict training set:
dtrain_predictions = alg.predict(dtrain[predictors])
dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]
    
#Print model report:
print("\nModel Report")
print("Accuracy : %.4g" % metrics.accuracy_score(dtrain['Disbursed'].values, dtrain_predictions))
print("AUC Score (Train): %f" % metrics.roc_auc_score(dtrain['Disbursed'], dtrain_predprob))
                
feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)
feat_imp.plot(kind='bar', title='Feature Importances')
plt.ylabel('Feature Importance Score')

Any further clarification required, please comment.TIA!