diff --git a/h2o-docs/src/product/data-science/gbm.rst b/h2o-docs/src/product/data-science/gbm.rst
index abadc2a86db9..99e65aad103b 100644
--- a/h2o-docs/src/product/data-science/gbm.rst
+++ b/h2o-docs/src/product/data-science/gbm.rst
@@ -358,6 +358,19 @@ Metrics
 
 Usage is illustrated in the Examples section.
 
+GBM Friedman and Popescu's H statistics
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+You can calculates the Friedman and Popescu's H statistics to test for the presence of an interaction between specified variables. 
+
+H varies from 0 to 1. It will have a value of 0 if the model exhibits no interaction between specified variables and a correspondingly larger value for a stronger interaction effect between them. NaN is returned if a computation is spoiled by weak main effects and rounding errors.
+
+This statistic can only be calculated for numerical variables. Missing values are supported.
+
+Reference implementation: `Python <https://pypi.org/project/sklearn-gbmi/>`__ and `R <https://rdrr.io/cran/gbm/man/interact.gbm.html>`__
+
+You can see how it used in the `Examples section <#examples>`__.
+
 Examples
 ~~~~~~~~
 
@@ -394,6 +407,10 @@ Below is a simple example showing how to build a Gradient Boosting Machine model
     # Extract feature interactions:
     feature_interactions <- h2o.feature_interaction(pros_gbm)
 
+    # Get Friedman and Popescu's H statistics
+    h <- h2o.h(pros_gbm, prostate, c('DPROS','DCAPS'))
+    print(h) 
+
 
    .. code-tab:: python
    
@@ -424,6 +441,10 @@ Below is a simple example showing how to build a Gradient Boosting Machine model
     # Extract feature interactions:
     feature_interactions = pros_gbm.feature_interaction()
 
+    # Get Friedman and Popescu's H statistics
+    h = pros_gbm.h(prostate_train, ['DPROS','DCAPS'])
+    print(h)
+
 
    .. code-tab:: scala
     
@@ -481,6 +502,8 @@ York, 2001. <http://statweb.stanford.edu/~tibs/ElemStatLearn/>`__
 
 `Nee, Daniel, "Calibrating Classifier Probabilities", 2014 <http://danielnee.com/tag/platt-scaling>`__
 
+`Jerome H. Friedman and Bogdan E. Popescu, 2008, "Predictive learning via rule ensembles", *Ann. Appl. Stat.* **2**:916-954. <http://projecteuclid.org/download/pdfview_1/euclid.aoas/1223908046>`__ 
+
 FAQ
 ~~~
 
diff --git a/h2o-docs/src/product/data-science/xgboost.rst b/h2o-docs/src/product/data-science/xgboost.rst
index 960952be1346..55d34687074b 100644
--- a/h2o-docs/src/product/data-science/xgboost.rst
+++ b/h2o-docs/src/product/data-science/xgboost.rst
@@ -373,6 +373,19 @@ Metrics
 
 Usage is illustrated in the Examples section.
 
+XGBoost Friedman and Popescu's H statistics
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+You can calculates the Friedman and Popescu's H statistics to test for the presence of an interaction between specified variables. 
+
+H varies from 0 to 1. It will have a value of 0 if the model exhibits no interaction between specified variables and a correspondingly larger value for a stronger interaction effect between them. NaN is returned if a computation is spoiled by weak main effects and rounding errors.
+
+This statistic can only be calculated for numerical variables. Missing values are supported.
+
+Reference implementation: `Python <https://pypi.org/project/sklearn-gbmi/>`__ and `R <https://rdrr.io/cran/gbm/man/interact.gbm.html>`__
+
+You can see how it used in the `Examples section <#examples>`__.
+
 Examples
 ~~~~~~~~
 
@@ -415,6 +428,10 @@ Below is a simple example showing how to build a XGBoost model.
     # Extract feature interactions:
     feature_interactions = h2o.feature_interaction(titanic_xgb)
 
+    # Get Friedman and Popescu's H statistics
+    h <- h2o.h(titanic_xgb, train, c('fair','age'))
+    print(h)
+
 
    .. code-tab:: python
    
@@ -451,6 +468,10 @@ Below is a simple example showing how to build a XGBoost model.
     # Extract feature interactions:
     feature_interactions = titanic_xgb.feature_interaction()
 
+    # Get Friedman and Popescu's H statistics
+    h = titanic_xgb.h(train, ['fair','age'])
+    print(h)
+
 Note
 ''''
 
@@ -507,4 +528,6 @@ References
 
 - Mitchell R, Frank E. (2017) Accelerating the XGBoost algorithm using GPU computing. PeerJ Preprints 5:e2911v1 `https://doi.org/10.7287/peerj.preprints.2911v1 <https://doi.org/10.7287/peerj.preprints.2911v1>`__
 
+`Jerome H. Friedman and Bogdan E. Popescu, 2008, "Predictive learning via rule ensembles", *Ann. Appl. Stat.* **2**:916-954. <http://projecteuclid.org/download/pdfview_1/euclid.aoas/1223908046>`__ 
+