diff --git a/docs/user_guide.rst b/docs/user_guide.rst
index 0c00e3e..b814795 100755
--- a/docs/user_guide.rst
+++ b/docs/user_guide.rst
@@ -119,17 +119,16 @@ Multi-target quantile regression is also supported. If the target values are mul
 Quantile Weighting
 ~~~~~~~~~~~~~~~~~~
 
-By default, the predict method calculates quantiles by weighting each sample inversely according to the size of its leaf node (`weighted_leaves = True`). If `weighted_leaves = False`, each sample in a leaf (including repeated bootstrap samples) will be given equal weight. Note that this leaf-based weighting can only be used with weighted quantiles.
-
 By default, the predict method calculates quantiles using a weighted quantile method (`weighted_quantile = True`), which assigns a weight to each sample in the training set based on the number of times that it co-occurs in the same leaves as the test sample. When the number of samples in the training set is larger than the expected size of this list (i.e., :math:`n_{train} \gg n_{trees} \cdot n_{leaves} \cdot n_{leafsamples}`), it can be more efficient to calculate an unweighted quantile (`weighted_quantile = False`), which aggregates the list of training `y` values for each leaf node to which the test sample belongs across all trees. For a given input, both methods can return the same output values::
 
     >>> import numpy as np
-    >>> kwargs = {"weighted_leaves": False}
-    >>> y_pred_weighted = reg.predict(X_test, weighted_quantile=True, **kwargs)
-    >>> y_pred_unweighted = reg.predict(X_test, weighted_quantile=False, **kwargs)
+    >>> y_pred_weighted = reg.predict(X_test, weighted_quantile=True)
+    >>> y_pred_unweighted = reg.predict(X_test, weighted_quantile=False)
     >>> np.allclose(y_pred_weighted, y_pred_unweighted)
     True
 
+By default, the predict method calculates quantiles by giving each sample in a leaf (including repeated bootstrap samples) equal weight (`weighted_leaves = False`). If `weighted_leaves = True`, each sample will be weighted inversely according to the size of its leaf node. Note that this leaf-based weighting can only be used with weighted quantiles.
+
 Out-of-Bag Estimation
 ~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/quantile_forest/_quantile_forest.py b/quantile_forest/_quantile_forest.py
index fe6a7c8..64a64c7 100755
--- a/quantile_forest/_quantile_forest.py
+++ b/quantile_forest/_quantile_forest.py
@@ -449,7 +449,7 @@ def predict(
         quantiles=None,
         interpolation="linear",
         weighted_quantile=True,
-        weighted_leaves=True,
+        weighted_leaves=False,
         aggregate_leaves_first=True,
         oob_score=False,
         indices=None,
@@ -490,7 +490,7 @@ def predict(
             number of training samples relative to siblings is small, weighted
             quantiles can be more efficient to compute than unweighted ones.
 
-        weighted_leaves : bool, default=True
+        weighted_leaves : bool, default=False
             Weight samples inversely to the size of their leaf node.
             Only used if `weighted_quantile=True` and `max_samples_leaf!=1`.
 
diff --git a/quantile_forest/_quantile_forest_fast.pyx b/quantile_forest/_quantile_forest_fast.pyx
index 35d0560..63288c8 100755
--- a/quantile_forest/_quantile_forest_fast.pyx
+++ b/quantile_forest/_quantile_forest_fast.pyx
@@ -617,7 +617,7 @@ cdef class QuantileForest:
         UINT8_t[:, :] X_indices=None,
         char* interpolation=b"linear",
         bint weighted_quantile=<bint>True,
-        bint weighted_leaves=<bint>True,
+        bint weighted_leaves=<bint>False,
         bint aggregate_leaves_first=<bint>True,
     ):
         """Return predictions for ``est.apply`` outputs.
@@ -644,7 +644,7 @@ cdef class QuantileForest:
         weighted_quantile : bool, default=True
             Calculate weighted quantiles.
 
-        weighted_leaves : bool, default=True
+        weighted_leaves : bool, default=False
             Weight samples inversely to the size of their leaf node.
 
         aggregate_leaves_first : bool, default=True