Deprecate reg:linear' in favor of reg:squarederror'. (#4267)

* Deprecate `reg:linear' in favor of `reg:squarederror'.
* Replace the use of `reg:linear'.
* Replace the use of `silent`.

demo/regression/README.md

@@ -6,9 +6,9 @@ Using XGBoost for regression is very similar to using it for binary classificati
 The dataset we used is the [computer hardware dataset from UCI repository](https://archive.ics.uci.edu/ml/datasets/Computer+Hardware). The demo for regression is almost the same as the [binary classification demo](../binary_classification), except a little difference in general parameter:
 ```
 # General parameter
-# this is the only difference with classification, use reg:linear to do linear regression
+# this is the only difference with classification, use reg:squarederror to do linear regression
 # when labels are in [0,1] we can also use reg:logistic
-objective = reg:linear
+objective = reg:squarederror
 ...
 
 ```

demo/regression/machine.conf

@@ -1,9 +1,9 @@
 # General Parameters, see comment for each definition
 # choose the tree booster, can also change to gblinear
 booster = gbtree
-# this is the only difference with classification, use reg:linear to do linear classification
+# this is the only difference with classification, use reg:squarederror to do linear classification
 # when labels are in [0,1] we can also use reg:logistic
-objective = reg:linear
+objective = reg:squarederror
 
 # Tree Booster Parameters
 # step size shrinkage

demo/yearpredMSD/yearpredMSD.conf

@@ -1,17 +1,17 @@
 # General Parameters, see comment for each definition
 # choose the tree booster, can also change to gblinear
 booster = gbtree
-# this is the only difference with classification, use reg:linear to do linear classification
+# this is the only difference with classification, use reg:squarederror to do linear classification
 # when labels are in [0,1] we can also use reg:logistic
-objective = reg:linear
+objective = reg:squarederror
 
 # Tree Booster Parameters
 # step size shrinkage
 eta = 1.0
 # minimum loss reduction required to make a further partition
-gamma = 1.0 
+gamma = 1.0
 # minimum sum of instance weight(hessian) needed in a child
-min_child_weight = 1 
+min_child_weight = 1
 # maximum depth of a tree
 max_depth = 5
 
@@ -20,11 +20,10 @@ base_score = 2001
 # the number of round to do boosting
 num_round = 100
 # 0 means do not save any model except the final round model
-save_period = 0 
+save_period = 0
 # The path of training data
 data = "yearpredMSD.libsvm.train"
 # The path of validation data, used to monitor training process, here [test] sets name of the validation set
 eval[test] = "yearpredMSD.libsvm.test"
-# The path of test data 
+# The path of test data
 #test:data = "yearpredMSD.libsvm.test"
-