Use bst_float consistently throughout (#1824)

* Fix various typos

* Add override to functions that are overridden

gcc gives warnings about functions that are being overridden by not
being marked as oveirridden. This fixes it.

* Use bst_float consistently

Use bst_float for all the variables that involve weight,
leaf value, gradient, hessian, gain, loss_chg, predictions,
base_margin, feature values.

In some cases, when due to additions and so on the value can
take a larger value, double is used.

This ensures that type conversions are minimal and reduces loss of
precision.

python-package/xgboost/training.py

@@ -148,7 +148,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
     evals_result: dict
         This dictionary stores the evaluation results of all the items in watchlist.
         Example: with a watchlist containing [(dtest,'eval'), (dtrain,'train')] and
-        a paramater containing ('eval_metric': 'logloss')
+        a parameter containing ('eval_metric': 'logloss')
         Returns: {'train': {'logloss': ['0.48253', '0.35953']},
                   'eval': {'logloss': ['0.480385', '0.357756']}}
     verbose_eval : bool or int
@@ -291,7 +291,7 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
        fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True,
        seed=0, callbacks=None):
     # pylint: disable = invalid-name
-    """Cross-validation with given paramaters.
+    """Cross-validation with given parameters.
 
     Parameters
     ----------