diff --git a/doc/parameter.md b/doc/parameter.md
index a549c25c9..77d42678e 100644
--- a/doc/parameter.md
+++ b/doc/parameter.md
@@ -54,22 +54,22 @@ Parameters for Tree Booster
 * alpha [default=0]
   - L1 regularization term on weights, increase this value will make model more conservative.
 * tree_method, string [default='auto']
-  - The tree constructtion algorithm used in XGBoost(see description in the [reference paper](http://arxiv.org/abs/1603.02754))
+  - The tree construction algorithm used in XGBoost(see description in the [reference paper](http://arxiv.org/abs/1603.02754))
   - Distributed and external memory version only support approximate algorithm.
   - Choices: {'auto', 'exact', 'approx'}
     - 'auto': Use heuristic to choose faster one.
       - For small to medium dataset, exact greedy will be used.
-      - For very large-dataset, approximate algorithm will be choosed.
+      - For very large-dataset, approximate algorithm will be chosen.
       - Because old behavior is always use exact greedy in single machine,
-        user will get a message when approximate algorithm is choosed to notify this choice.
+        user will get a message when approximate algorithm is chosen to notify this choice.
     - 'exact': Exact greedy algorithm.
     - 'approx': Approximate greedy algorithm using sketching and histogram.
 * sketch_eps, [default=0.03]
   - This is only used for approximate greedy algorithm.
   - This roughly translated into ```O(1 / sketch_eps)``` number of bins.
-    Compared to directly select number of bins, this comes with theoretical ganrantee with sketch accuracy.
-  - Usuaully user do not have to tune this.
-    but consider set to lower number for more accurate enumeration.
+    Compared to directly select number of bins, this comes with theoretical guarantee with sketch accuracy.
+  - Usually user does not have to tune this.
+    but consider setting to a lower number for more accurate enumeration.
   - range: (0, 1)
 * scale_pos_weight, [default=0]
   - Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative  cases) / sum(positive cases) See [Parameters Tuning](how_to/param_tuning.md) for more discussion. Also see Higgs Kaggle competition demo for examples: [R](../demo/kaggle-higgs/higgs-train.R ), [py1](../demo/kaggle-higgs/higgs-numpy.py ), [py2](../demo/kaggle-higgs/higgs-cv.py ), [py3](../demo/guide-python/cross_validation.py)
@@ -121,7 +121,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
  - "reg:gamma" --gamma regression for severity data, output mean of gamma distribution
 * base_score [ default=0.5 ]
   - the initial prediction score of all instances, global bias
-  - for sufficent number of iterations, changing this value will not have too much effect.
+  - for sufficient number of iterations, changing this value will not have too much effect.
 * eval_metric [ default according to objective ]
   - evaluation metrics for validation data, a default metric will be assigned according to objective( rmse for regression, and error for classification, mean average precision for ranking )
   - User can add multiple evaluation metrics, for python user, remember to pass the metrics in as list of parameters pairs instead of map, so that latter 'eval_metric' won't override previous one
@@ -137,7 +137,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
   - "map":[Mean average precision](http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision)
   - "ndcg@n","map@n": n can be assigned as an integer to cut off the top positions in the lists for evaluation.
   - "ndcg-","map-","ndcg@n-","map@n-": In XGBoost, NDCG and MAP will evaluate the score of a list without any positive samples as 1. By adding "-" in the evaluation metric XGBoost will evaluate these score as 0 to be consistent under some conditions.
-training repeatively
+training repeatedly
   - "gamma-deviance": [residual deviance for gamma regression]
 * seed [ default=0 ]
  - random number seed.