Address some sphinx warnings and errors, add doc for building doc. (#4589)

doc/jvm/xgboost4j_spark_tutorial.rst

@@ -154,7 +154,7 @@ Now, we have a DataFrame containing only two columns, "features" which contains
 labels. A DataFrame like this (containing vector-represented features and numeric labels) can be fed to XGBoost4J-Spark's training engine directly.
 
 Dealing with missing values
-~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Strategies to handle missing values (and therefore overcome issues as above):
 
@@ -244,7 +244,7 @@ When it comes to custom eval metrics, in additional to ``num_early_stopping_roun
 For example, we need to maximize the evaluation metrics (set ``maximize_evaluation_metrics`` with true), and set ``num_early_stopping_rounds`` with 5. The evaluation metric of 10th iteration is the maximum one until now. In the following iterations, if there is no evaluation metric greater than the 10th iteration's (best one), the traning would be early stopped at 15th iteration.  
 
 Training with Evaluation Sets
-----------------
+-----------------------------
 
 You can also monitor the performance of the model during training with multiple evaluation datasets. By specifying ``eval_sets`` or call ``setEvalSets`` over a XGBoostClassifier or XGBoostRegressor, you can pass in multiple evaluation datasets typed as a Map from String to DataFrame.