diff --git a/examples/0.basics.py b/examples/0.basics.py
index a5ce67d..a891475 100644
--- a/examples/0.basics.py
+++ b/examples/0.basics.py
@@ -6,6 +6,7 @@ import numpy as np
 from sklearn.linear_model import LogisticRegression
 
 import quapy as qp
+from quapy.method.aggregative import PACC
 
 # let's fetch some dataset to run one experiment
 # datasets are available in the "qp.data.datasets" module (there is a shortcut in qp.datasets)
@@ -34,7 +35,7 @@ print(f'training prevalence = {F.strprev(train.prevalence())}')
 
 # let us train one quantifier, for example, PACC using a sklearn's Logistic Regressor as the underlying classifier
 classifier = LogisticRegression()
-pacc = qp.method.aggregative.PACC(classifier)
+pacc = PACC(classifier)
 
 print(f'training {pacc}')
 pacc.fit(X, y)
diff --git a/examples/1.model_selection.py b/examples/1.model_selection.py
index 47a7620..6c96671 100644
--- a/examples/1.model_selection.py
+++ b/examples/1.model_selection.py
@@ -24,7 +24,8 @@ print(f'running model selection with N_JOBS={qp.environ["N_JOBS"]}; '
 training, test = qp.datasets.fetch_UCIMulticlassDataset('letter').train_test
 
 # evaluation in terms of MAE with default hyperparameters
-model.fit(*training.Xy)
+Xtr, ytr = training.Xy
+model.fit(Xtr, ytr)
 mae_score = qp.evaluation.evaluate(model, protocol=UPP(test), error_metric='mae')
 print(f'MAE (non optimized)={mae_score:.5f}')