[docs] fixing max_depth param description (#2155)

* PR #1879

* Update docs with parameter_generator.py

* Update wrapper doc for sklearn

docs/Parameters.rst

@@ -190,7 +190,7 @@ Learning Control Parameters
 
    -  limit the max depth for tree model. This is used to deal with over-fitting when ``#data`` is small. Tree still grows leaf-wise
 
-   -  ``< 0`` means no limit
+   -  ``<= 0`` means no limit
 
 -  ``min_data_in_leaf`` :raw-html:`<a id="min_data_in_leaf" title="Permalink to this parameter" href="#min_data_in_leaf">&#x1F517;&#xFE0E;</a>`, default = ``20``, type = int, aliases: ``min_data_per_leaf``, ``min_data``, ``min_child_samples``, constraints: ``min_data_in_leaf >= 0``
 

include/LightGBM/config.h

@@ -212,7 +212,7 @@ struct Config {
   #pragma region Learning Control Parameters
 
   // desc = limit the max depth for tree model. This is used to deal with over-fitting when ``#data`` is small. Tree still grows leaf-wise
-  // desc = ``< 0`` means no limit
+  // desc = ``<= 0`` means no limit
   int max_depth = -1;
 
   // alias = min_data_per_leaf, min_data, min_child_samples

python-package/lightgbm/sklearn.py

@@ -152,7 +152,7 @@ class LGBMModel(_LGBMModelBase):
         num_leaves : int, optional (default=31)
             Maximum tree leaves for base learners.
         max_depth : int, optional (default=-1)
-            Maximum tree depth for base learners, -1 means no limit.
+            Maximum tree depth for base learners, <=0 means no limit.
         learning_rate : float, optional (default=0.1)
             Boosting learning rate.
             You can use ``callbacks`` parameter of ``fit`` method to shrink/adapt learning rate