[docs] added note about shap package (#1620)

docs/Parameters.rst

@@ -597,6 +597,8 @@ IO Parameters
 
    -  produces ``#features + 1`` values where the last value is the expected value of the model output over the training data
 
+   -  **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package <https://github.com/slundberg/shap>`__
+
 -  ``num_iteration_predict`` :raw-html:`<a id="num_iteration_predict" title="Permalink to this parameter" href="#num_iteration_predict">&#x1F517;&#xFE0E;</a>`, default = ``-1``, type = int
 
    -  used only in ``prediction`` task

include/LightGBM/config.h

@@ -557,6 +557,7 @@ public:
   // desc = used only in ``prediction`` task
   // desc = set this to ``true`` to estimate `SHAP values <https://arxiv.org/abs/1706.06060>`__, which represent how each feature contributes to each prediction
   // desc = produces ``#features + 1`` values where the last value is the expected value of the model output over the training data
+  // desc = **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package <https://github.com/slundberg/shap>`__
   bool predict_contrib = false;
 
   // desc = used only in ``prediction`` task

python-package/lightgbm/basic.py

@@ -2062,6 +2062,13 @@ class Booster(object):
             Whether to predict leaf index.
         pred_contrib : bool, optional (default=False)
             Whether to predict feature contributions.
+
+            Note
+            ----
+            If you want to get more explanation for your model's predictions using SHAP values
+            like SHAP interaction values,
+            you can install shap package (https://github.com/slundberg/shap).
+
         data_has_header : bool, optional (default=False)
             Whether the data has header.
             Used only if data is string.

python-package/lightgbm/sklearn.py

@@ -528,6 +528,13 @@ class LGBMModel(_LGBMModelBase):
             Whether to predict leaf index.
         pred_contrib : bool, optional (default=False)
             Whether to predict feature contributions.
+
+            Note
+            ----
+            If you want to get more explanation for your model's predictions using SHAP values
+            like SHAP interaction values,
+            you can install shap package (https://github.com/slundberg/shap).
+
         **kwargs : other parameters for the prediction
 
         Returns
@@ -750,6 +757,13 @@ class LGBMClassifier(LGBMModel, _LGBMClassifierBase):
             Whether to predict leaf index.
         pred_contrib : bool, optional (default=False)
             Whether to predict feature contributions.
+
+            Note
+            ----
+            If you want to get more explanation for your model's predictions using SHAP values
+            like SHAP interaction values,
+            you can install shap package (https://github.com/slundberg/shap).
+
         **kwargs : other parameters for the prediction
 
         Returns