scikit-learn
diff --git a/‎sklearn/ensemble/_iforest.py
Lines changed: 1 addition & 1 deletion b/‎sklearn/ensemble/_iforest.py
Lines changed: 1 addition & 1 deletion
diff --git a/‎sklearn/ensemble/tests/test_iforest.py
Lines changed: 30 additions & 29 deletions b/‎sklearn/ensemble/tests/test_iforest.py
Lines changed: 30 additions & 29 deletions
@@ -331,7 +331,7 @@ def predict(self, X):
         check_is_fitted(self)
         X = check_array(X, accept_sparse='csr')
         is_inlier = np.ones(X.shape[0], dtype=int)
-        is_inlier[self.decision_function(X) < 0] = -1
+        is_inlier[self.decision_function(X) < -1e-15] = -1
         return is_inlier
 
     def decision_function(self, X):
 
@@ -327,32 +327,33 @@ def test_iforest_deprecation():
 def test_iforest_with_uniform_data():
     """Test whether iforest predicts inliers when using uniform data"""
 
-    # 2-d array of all 1s
-    X = np.ones((100, 10))
-    iforest = IsolationForest()
-    iforest.fit(X)
-
-    rng = np.random.RandomState(0)
-
-    assert all(iforest.predict(X) == 1)
-    assert all(iforest.predict(rng.randn(100, 10)) == 1)
-    assert all(iforest.predict(X + 1) == 1)
-    assert all(iforest.predict(X - 1) == 1)
-
-    # 2-d array where columns contain the same value across rows
-    X = np.repeat(rng.randn(1, 10), 100, 0)
-    iforest = IsolationForest()
-    iforest.fit(X)
-
-    assert all(iforest.predict(X) == 1)
-    assert all(iforest.predict(rng.randn(100, 10)) == 1)
-    assert all(iforest.predict(np.ones((100, 10))) == 1)
-
-    # Single row
-    X = rng.randn(1, 10)
-    iforest = IsolationForest()
-    iforest.fit(X)
-
-    assert all(iforest.predict(X) == 1)
-    assert all(iforest.predict(rng.randn(100, 10)) == 1)
-    assert all(iforest.predict(np.ones((100, 10))) == 1)
+    for n_samples, n_features in np.random.randint(10, 200, size=(10, 2)):
+        # 2-d array of all 1s
+        X = np.ones((n_samples, n_features))
+        iforest = IsolationForest()
+        iforest.fit(X)
+
+        rng = np.random.RandomState(0)
+
+        assert all(iforest.predict(X) == 1)
+        assert all(iforest.predict(rng.randn(n_samples, n_features)) == 1)
+        assert all(iforest.predict(X + 1) == 1)
+        assert all(iforest.predict(X - 1) == 1)
+
+        # 2-d array where columns contain the same value across rows
+        X = np.repeat(rng.randn(1, n_features), n_samples, 0)
+        iforest = IsolationForest()
+        iforest.fit(X)
+
+        assert all(iforest.predict(X) == 1)
+        assert all(iforest.predict(rng.randn(n_samples, n_features)) == 1)
+        assert all(iforest.predict(np.ones((n_samples, n_features))) == 1)
+
+        # Single row
+        X = rng.randn(1, n_features)
+        iforest = IsolationForest()
+        iforest.fit(X)
+
+        assert all(iforest.predict(X) == 1)
+        assert all(iforest.predict(rng.randn(n_samples, n_features)) == 1)
+        assert all(iforest.predict(np.ones((n_samples, n_features))) == 1)