Fixing a few typos (#1771)

* Fixing a few typos

* Fixing a few typos

doc/R-package/discoverYourData.md

@@ -417,14 +417,14 @@ The case studied here is not enough complex to show that. Check [Kaggle website]
 
 Moreover, you can notice that even if we have added some not useful new features highly correlated with other features, the boosting tree algorithm have been able to choose the best one, which in this case is the Age.
 
-Linear model may not be that smart in this scenario.
+Linear models may not be that smart in this scenario.
 
 Special Note: What about Random Forests™?
 -----------------------------------------
 
 As you may know, [Random Forests™](http://en.wikipedia.org/wiki/Random_forest) algorithm is cousin with boosting and both are part of the [ensemble learning](http://en.wikipedia.org/wiki/Ensemble_learning) family.
 
-Both trains several decision trees for one dataset. The *main* difference is that in Random Forests™, trees are independent and in boosting, the tree `N+1` focus its learning on the loss (<=> what has not been well modeled by the tree `N`).
+Both train several decision trees for one dataset. The *main* difference is that in Random Forests™, trees are independent and in boosting, the tree `N+1` focus its learning on the loss (<=> what has not been well modeled by the tree `N`).
 
 This difference have an impact on a corner case in feature importance analysis: the *correlated features*.
 

doc/README

@@ -1,7 +1,7 @@
-The document of xgboost is generated with recommonmark and sphinx.
+The documentation of xgboost is generated with recommonmark and sphinx.
 
 You can build it locally by typing "make html" in this folder.
 - clone https://github.com/tqchen/recommonmark to root
 - type make html
 
-Checkout https://recommonmark.readthedocs.org for guide on how to write markdown with extensions used in this doc, such as math formulas and table of content.
+Checkout https://recommonmark.readthedocs.org for guide on how to write markdown with extensions used in this doc, such as math formulas and table of content.

doc/faq.md

@@ -56,7 +56,7 @@ Yes, xgboost implements LambdaMART. Checkout the objective section in [parameter
 
 How to deal with Missing Value
 ------------------------------
-xgboost support missing value by default
+xgboost supports missing value by default.
 
 
 Slightly different result between runs

doc/get_started/index.md

@@ -1,6 +1,6 @@
 # Get Started with XGBoost
 
-This is a quick started tutorial showing snippets for you to quickly try out xgboost
+This is a quick start tutorial showing snippets for you to quickly try out xgboost
 on the demo dataset on a binary classification task.
 
 ## Links to Helpful Other Resources

doc/parameter.md

@@ -8,7 +8,7 @@ Before running XGboost, we must set three types of parameters: general parameter
 
 Parameters in R Package
 -----------------------
-In R-package, you can use .(dot) to replace under score in the parameters, for example, you can use max.depth as max_depth. The underscore parameters are also valid in R.
+In R-package, you can use .(dot) to replace underscore in the parameters, for example, you can use max.depth as max_depth. The underscore parameters are also valid in R.
 
 General Parameters
 ------------------
@@ -29,13 +29,13 @@ Parameters for Tree Booster
   - step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features. and eta actually shrinks the feature weights to make the boosting process more conservative.
   - range: [0,1]
 * gamma [default=0]
-  - minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be.
+  - minimum loss reduction required to make a further partition on a leaf node of the tree. The larger, the more conservative the algorithm will be.
   - range: [0,∞]
 * max_depth [default=6]
-  - maximum depth of a tree, increase this value will make model more complex / likely to be overfitting.
+  - maximum depth of a tree, increase this value will make the model more complex / likely to be overfitting.
   - range: [1,∞]
 * min_child_weight [default=1]
-  - minimum sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be.
+  - minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be.
   - range: [0,∞]
 * max_delta_step [default=0]
   - Maximum delta step we allow each tree's weight estimation to be. If the value is set to 0, it means there is no constraint. If it is set to a positive value, it can help making the update step more conservative. Usually this parameter is not needed, but it might help in logistic regression when class is extremely imbalanced. Set it to value of 1-10 might help control the update
@@ -85,7 +85,7 @@ Additional parameters for Dart Booster
 * normalize_type [default="tree"]
   - type of normalization algorithm.
     - "tree": new trees have the same weight of each of dropped trees.
-      - weight of new trees are 1 / (k + learnig_rate)
+      - weight of new trees are 1 / (k + learning_rate)
       - dropped trees are scaled by a factor of k / (k + learning_rate)
     - "forest": new trees have the same weight of sum of dropped trees (forest).
       - weight of new trees are 1 / (1 + learning_rate)
@@ -105,10 +105,10 @@ Parameters for Linear Booster
 * alpha [default=0]
   - L1 regularization term on weights, increase this value will make model more conservative.
 * lambda_bias
-  - L2 regularization term on bias, default 0(no L1 reg on bias because it is not important)
+  - L2 regularization term on bias, default 0 (no L1 reg on bias because it is not important)
 
 Parameters for Tweedie Regression
------------------------------
+---------------------------------
 * tweedie_variance_power [default=1.5]
   - Parameter that controls the variance of the tweedie distribution.  Set closer to 2 to shift towards a gamma distribution and closer to 1 to shift towards a poisson distribution.
 
@@ -132,7 +132,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
   - the initial prediction score of all instances, global bias
   - 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 )
+  - 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
   - The choices are listed below:
   - "rmse": [root mean square error](http://en.wikipedia.org/wiki/Root_mean_square_error)
@@ -163,12 +163,12 @@ The following parameters are only used in the console version of xgboost
 * test:data
   - The path of test data to do prediction
 * save_period [default=0]
- - the period to save the model, setting save_period=10 means that for every 10 rounds XGBoost will save the model, setting it to 0 means not save any model during training.
+ - the period to save the model, setting save_period=10 means that for every 10 rounds XGBoost will save the model, setting it to 0 means not saving any model during the training.
 * task [default=train] options: train, pred, eval, dump
   - train: training using data
   - pred: making prediction for test:data
   - eval: for evaluating statistics specified by eval[name]=filename
-  - dump: for dump the learned model into text format(preliminary)
+  - dump: for dump the learned model into text format (preliminary)
 * model_in [default=NULL]
   - path to input model, needed for test, eval, dump, if it is specified in training, xgboost will continue training from the input model
 * model_out [default=NULL]