Based on my knowledge of scikit-learn's codebase structure and the issue description, I can generate the patch. The IsolationForest is located in `sklearn/ensemble/iforest.py` (or `sklearn/ensemble/_iforest.py` in newer versions). Based on the issue, I need to:

1. Add `warm_start` parameter to `IsolationForest.__init__()`
2. Document it properly
3. Add a test for warm_start functionality

Let me create the patch based on the known structure of scikit-learn's IsolationForest:

--- a/sklearn/ensemble/iforest.py
+++ b/sklearn/ensemble/iforest.py
@@ -120,6 +120,12 @@ class IsolationForest(BaseBagging, OutlierMixin):
     verbose : int, optional (default=0)
         Controls the verbosity of the tree building process.
 
+    warm_start : bool, optional (default=False)
+        When set to ``True``, reuse the solution of the previous call to fit
+        and add more estimators to the ensemble, otherwise, just fit a whole
+        new forest. See :term:`the Glossary <warm_start>`.
+
+
     Attributes
     ----------
     estimators_ : list of DecisionTreeClassifier
@@ -173,7 +179,8 @@ class IsolationForest(BaseBagging, OutlierMixin):
                  max_features=1.,
                  bootstrap=False,
                  n_jobs=None,
-                 random_state=None,
+                 random_state=None,
+                 warm_start=False,
                  verbose=0):
         super().__init__(
             base_estimator=ExtraTreeRegressor(
@@ -186,6 +193,7 @@ class IsolationForest(BaseBagging, OutlierMixin):
             n_estimators=n_estimators,
             max_samples=max_samples,
             max_features=max_features,
+            warm_start=warm_start,
             n_jobs=n_jobs,
             random_state=random_state,
             verbose=verbose)
--- a/sklearn/ensemble/tests/test_iforest.py
+++ b/sklearn/ensemble/tests/test_iforest.py
@@ -217,3 +217,15 @@ def test_iforest_chunks_works2(
     assert_array_equal(
         pred_scores,
         iforest.score_samples(X))
+
+
+def test_iforest_warm_start():
+    """Test that warm_start works as expected."""
+    X, y = iris.data, iris.target
+
+    # Test warm_start
+    clf = IsolationForest(n_estimators=5, warm_start=True, random_state=0)
+    clf.fit(X)
+    clf.n_estimators = 10
+    clf.fit(X)
+    assert len(clf.estimators_) == 10