How to solve this "AssertionError: Must have at least 1 validation dataset for early stopping." in xgboost model=1.6.2

Question

from skopt import BayesSearchCV bayes_cv_tuner = BayesSearchCV( estimator = XGBClassifier( n_jobs = 8, objective = 'binary:logistic', eval_metric = 'auc', silent=1, tree_method='approx', early_stopping_rounds = 10 ), search_spaces = { 'learning_rate': (0.001, 0.005), 'min_child_weight': (150, 500), 'max_depth': (2, 4), 'max_delta_step': (1, 10), 'subsample': (0.5, .9), 'colsample_bytree': (0.6, 0.8), 'colsample_bylevel': (0.6, 0.9), 'reg_lambda':(100,500), 'reg_alpha': (0.005,.05), 'gamma': (2.5,10), 'n_estimators': (25, 100), 'scale_pos_weight': (1, 1000) }, scoring = 'roc_auc', cv = StratifiedKFold( n_splits=5, shuffle=True, random_state=42 ), n_jobs = 8, n_iter = 80, refit = True, random_state = 42 ) def status_print(optim_result): """Status callback during bayesian hyperparameter search""" # Get all the models tested so far in DataFrame format all_models = pd.DataFrame(bayes_cv_tuner.cv_results_) # Get current parameters and the best parameters best_params = pd.Series(bayes_cv_tuner.best_params_) print('Model #{}\nBest ROC-AUC: {}\nBest params: {}\n'.format( len(all_models), np.round(bayes_cv_tuner.best_score_, 4), bayes_cv_tuner.best_params_ )) # Save all model results clf_name = bayes_cv_tuner.estimator._class_._name_ all_models.to_csv(clf_name+"_cv_results.csv")

result = bayes_cv_tuner.fit(data_train.iloc[:,1:-1],data_train.iloc[:,-1], callback=status_print)

Answer 1

You need add eval_set in fit function:

evalset = [(feature_validation, target_validation)]
result = bayes_cv_tuner.fit(data_train.iloc[:,1:-1],data_train.iloc[:,-1], eval_set=evalset, callback=status_prin)