Fix typos (#9731)

doc/faq.rst

@@ -10,14 +10,14 @@ How to tune parameters
 See :doc:`Parameter Tuning Guide </tutorials/param_tuning>`.
 
 ************************
-Description on the model
+Description of the model
 ************************
 See :doc:`Introduction to Boosted Trees </tutorials/model>`.
 
 ********************
 I have a big dataset
 ********************
-XGBoost is designed to be memory efficient. Usually it can handle problems as long as the data fit into your memory.
+XGBoost is designed to be memory efficient. Usually it can handle problems as long as the data fits into your memory.
 This usually means millions of instances.
 
 If you are running out of memory, checkout the tutorial page for using :doc:`distributed training </tutorials/index>` with one of the many frameworks, or the :doc:`external memory version </tutorials/external_memory>` for using external memory.
@@ -26,7 +26,7 @@ If you are running out of memory, checkout the tutorial page for using :doc:`dis
 **********************************
 How to handle categorical feature?
 **********************************
-Visit :doc:`this tutorial </tutorials/categorical>` for a walk through of categorical data handling and some worked examples.
+Visit :doc:`this tutorial </tutorials/categorical>` for a walkthrough of categorical data handling and some worked examples.
 
 ******************************************************************
 Why not implement distributed XGBoost on top of X (Spark, Hadoop)?
@@ -37,14 +37,14 @@ The ultimate question will still come back to how to push the limit of each comp
 and use less resources to complete the task (thus with less communication and chance of failure).
 
 To achieve these, we decide to reuse the optimizations in the single node XGBoost and build the distributed version on top of it.
-The demand of communication in machine learning is rather simple, in the sense that we can depend on a limited set of APIs (in our case rabit).
+The demand for communication in machine learning is rather simple, in the sense that we can depend on a limited set of APIs (in our case rabit).
 Such design allows us to reuse most of the code, while being portable to major platforms such as Hadoop/Yarn, MPI, SGE.
 Most importantly, it pushes the limit of the computation resources we can use.
 
 ****************************************
 How can I port a model to my own system?
 ****************************************
-The model and data format of XGBoost is exchangeable,
+The model and data format of XGBoost are exchangeable,
 which means the model trained by one language can be loaded in another.
 This means you can train the model using R, while running prediction using
 Java or C++, which are more common in production systems.

doc/parameter.rst

@@ -73,7 +73,7 @@ Parameters for Tree Booster
 ===========================
 * ``eta`` [default=0.3, alias: ``learning_rate``]
 
-  - Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and ``eta`` shrinks the feature weights to make the boosting process more conservative.
+  - Step size shrinkage used in update to prevent overfitting. After each boosting step, we can directly get the weights of new features, and ``eta`` shrinks the feature weights to make the boosting process more conservative.
   - range: [0,1]
 
 * ``gamma`` [default=0, alias: ``min_split_loss``]

python-package/xgboost/spark/params.py

@@ -17,7 +17,7 @@ class HasArbitraryParamsDict(Params):
         Params._dummy(),
         "arbitrary_params_dict",
         "arbitrary_params_dict This parameter holds all of the additional parameters which are "
-        "not exposed as the the XGBoost Spark estimator params but can be recognized by "
+        "not exposed as the XGBoost Spark estimator params but can be recognized by "
         "underlying XGBoost library. It is stored as a dictionary.",
     )
 

rabit/src/allreduce_base.cc

@@ -106,7 +106,7 @@ bool AllreduceBase::Init(int argc, char* argv[]) {
     }
   }
   if (dmlc_role != "worker") {
-    LOG(FATAL) << "Rabit Module currently only work with dmlc worker";
+    LOG(FATAL) << "Rabit Module currently only works with dmlc worker";
   }
 
   // clear the setting before start reconnection
@@ -273,7 +273,7 @@ void AllreduceBase::SetParam(const char *name, const char *val) {
   return xgboost::collective::Success();
 }
 /*!
- * \brief connect to the tracker to fix the the missing links
+ * \brief connect to the tracker to fix the missing links
  *   this function is also used when the engine start up
  */
 [[nodiscard]] xgboost::collective::Result AllreduceBase::ReConnectLinks(const char *cmd) {

rabit/src/allreduce_base.h

@@ -89,7 +89,7 @@ class AllreduceBase : public IEngine {
   }
 
   /*!
-  * \brief internal Allgather function, each node have a segment of data in the ring of sendrecvbuf,
+  * \brief internal Allgather function, each node has a segment of data in the ring of sendrecvbuf,
   *  the data provided by current node k is [slice_begin, slice_end),
   *  the next node's segment must start with slice_end
   *  after the call of Allgather, sendrecvbuf_ contains all the contents including all segments
@@ -281,7 +281,7 @@ class AllreduceBase : public IEngine {
      * this function can not be used together with ReadToRingBuffer
      * a link can either read into the ring buffer, or existing array
      * \param max_size maximum size of array
-     * \return true if it is an successful read, false if there is some error happens, check errno
+     * \return true if it is a successful read, false if there is some error happens, check errno
      */
     inline ReturnType ReadToArray(void *recvbuf_, size_t max_size) {
       if (max_size == size_read) return kSuccess;
@@ -299,7 +299,7 @@ class AllreduceBase : public IEngine {
      * \brief write data in array to sock
      * \param sendbuf_ head of array
      * \param max_size maximum size of array
-     * \return true if it is an successful write, false if there is some error happens, check errno
+     * \return true if it is a successful write, false if there is some error happens, check errno
      */
     inline ReturnType WriteFromArray(const void *sendbuf_, size_t max_size) {
       const char *p = static_cast<const char*>(sendbuf_);
@@ -333,7 +333,7 @@ class AllreduceBase : public IEngine {
    */
   [[nodiscard]] xgboost::collective::Result ConnectTracker(xgboost::collective::TCPSocket *out) const;
   /*!
-   * \brief connect to the tracker to fix the the missing links
+   * \brief connect to the tracker to fix the missing links
    *   this function is also used when the engine start up
    * \param cmd possible command to sent to tracker
    */
@@ -358,7 +358,7 @@ class AllreduceBase : public IEngine {
                           size_t count,
                           ReduceFunction reducer);
   /*!
-   * \brief broadcast data from root to all nodes, this function can fail,and will return the cause of failure
+   * \brief broadcast data from root to all nodes, this function can fail, and will return the cause of failure
    * \param sendrecvbuf_ buffer for both sending and receiving data
    * \param size the size of the data to be broadcasted
    * \param root the root worker id to broadcast the data