diff --git a/quapy/data/base.py b/quapy/data/base.py
index e52230e..ceb7402 100644
--- a/quapy/data/base.py
+++ b/quapy/data/base.py
@@ -549,7 +549,7 @@ class Dataset:
             yield Dataset(train, test, name=f'fold {(i % nfolds) + 1}/{nfolds} (round={(i // nfolds) + 1})')
 
 
-    def reduce(self, n_train=100, n_test=100):
+    def reduce(self, n_train=100, n_test=100, random_state=None):
         """
         Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.
 
@@ -557,6 +557,14 @@ class Dataset:
         :param n_test: number of test documents to keep (default 100)
         :return: self
         """
-        self.training = self.training.sampling(n_train, *self.training.prevalence())
-        self.test = self.test.sampling(n_test, *self.test.prevalence())
+        self.training = self.training.sampling(
+            n_train,
+            *self.training.prevalence(),
+            random_state = random_state
+        )
+        self.test = self.test.sampling(
+            n_test,
+            *self.test.prevalence(),
+            random_state = random_state
+        )
         return self
\ No newline at end of file
diff --git a/quapy/tests/test_modsel.py b/quapy/tests/test_modsel.py
index bf3e6f2..64b0ff4 100644
--- a/quapy/tests/test_modsel.py
+++ b/quapy/tests/test_modsel.py
@@ -19,7 +19,7 @@ class ModselTestCase(unittest.TestCase):
 
         q = PACC(LogisticRegression(random_state=1, max_iter=5000))
 
-        data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10).reduce()
+        data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10).reduce(random_state=1)
         training, validation = data.training.split_stratified(0.7, random_state=1)
 
         param_grid = {'classifier__C': [0.000001, 10.]}
@@ -41,7 +41,7 @@ class ModselTestCase(unittest.TestCase):
 
         q = PACC(LogisticRegression(random_state=1, max_iter=5000))
 
-        data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10).reduce(n_train=500)
+        data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10).reduce(n_train=500, random_state=1)
         training, validation = data.training.split_stratified(0.7, random_state=1)
 
         param_grid = {'classifier__C': np.logspace(-3,3,7)}
@@ -79,7 +79,7 @@ class ModselTestCase(unittest.TestCase):
 
         q = PACC(SlowLR())
 
-        data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10).reduce()
+        data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10).reduce(random_state=1)
         training, validation = data.training.split_stratified(0.7, random_state=1)
 
         param_grid = {'classifier__C': np.logspace(-1,1,3)}