[23/50] [abbrv] lucene-solr:jira/http2: SOLR-11863: Fix RefGuide typos

[da...@apache.org]

SOLR-11863: Fix RefGuide typos


Project: http://git-wip-us.apache.org/repos/asf/lucene-solr/repo
Commit: http://git-wip-us.apache.org/repos/asf/lucene-solr/commit/719d922c
Tree: http://git-wip-us.apache.org/repos/asf/lucene-solr/tree/719d922c
Diff: http://git-wip-us.apache.org/repos/asf/lucene-solr/diff/719d922c

Branch: refs/heads/jira/http2
Commit: 719d922cbc8acb7594f1f7862c917cbc70fda341
Parents: 0113ade
Author: Joel Bernstein <jb...@apache.org>
Authored: Wed Sep 5 11:30:27 2018 -0400
Committer: Joel Bernstein <jb...@apache.org>
Committed: Wed Sep 5 11:30:27 2018 -0400

----------------------------------------------------------------------
 solr/solr-ref-guide/src/machine-learning.adoc | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/lucene-solr/blob/719d922c/solr/solr-ref-guide/src/machine-learning.adoc
----------------------------------------------------------------------
diff --git a/solr/solr-ref-guide/src/machine-learning.adoc b/solr/solr-ref-guide/src/machine-learning.adoc
index ae781bb..ca0ae74 100644
--- a/solr/solr-ref-guide/src/machine-learning.adoc
+++ b/solr/solr-ref-guide/src/machine-learning.adoc
@@ -723,9 +723,9 @@ The `knnRegress` function prepares the training set for use with the `predict` f
 
 Below is an example of the `knnRegress` function. In this example 10000 random samples
 are taken each containing the variables *filesize_d*, *service_d* and *response_d*. The pairs of
-*filesize_d* and *service_d* will be use to predict the value of *response_d*.
+*filesize_d* and *service_d* will be used to predict the value of *response_d*.
 
-Notice that `kknRegress` simply returns a tuple describing the regression inputs.
+Notice that `knnRegress` returns a tuple describing the regression inputs.
 
 [source,text]
 ----
@@ -765,7 +765,7 @@ This expression returns the following response:
 
 === Prediction and Residuals
 
-The output of knnRegress can be used with the `predict` function like other regression models.
+The output of `knnRegress` can be used with the `predict` function like other regression models.
 In the example below the `predict` function is used to predict results for the original training
 data. The sumSq of the residuals is then calculated.
 
@@ -808,6 +808,7 @@ will carry more weight in the distance calculation then the smaller features. Th
 impact the accuracy of the prediction. The `knnRegress` function has a *scale* parameter which
 can be set to *true* to automatically scale the features in the same range.
 
+The example below shows `knnRegress` with feature scaling turned on.
 Notice that when feature scaling is turned on the sumSqErr in the output is much lower.
 This shows how much more accurate the predictions are when feature scaling is turned on in
 this particular example. This is because the *filesize_d* feature is significantly larger then
@@ -856,7 +857,7 @@ This provides a regression prediction that is robust to outliers.
 
 === Setting the Distance Measure
 
-The distance measure can be changed for the k-nearest neighbor search by adding distance measure
+The distance measure can be changed for the k-nearest neighbor search by adding a distance measure
 function to the `knnRegress` parameters. Below is an example using manhattan distance.
 
 [source,text]