[lucene-solr] branch SOLR-13105-visual updated: SOLR-13105: Proof machine learning docs 1

[jb...@apache.org]

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jbernste pushed a commit to branch SOLR-13105-visual
in repository https://gitbox.apache.org/repos/asf/lucene-solr.git


The following commit(s) were added to refs/heads/SOLR-13105-visual by this push:
     new fcf5421  SOLR-13105: Proof machine learning docs 1
fcf5421 is described below

commit fcf5421494cbc4153d8300654eb85a9844e8d7e7
Author: Joel Bernstein <jb...@apache.org>
AuthorDate: Sun Oct 13 21:35:42 2019 -0400

    SOLR-13105: Proof machine learning docs 1
---
 solr/solr-ref-guide/src/machine-learning.adoc | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/solr/solr-ref-guide/src/machine-learning.adoc b/solr/solr-ref-guide/src/machine-learning.adoc
index ff20cbb..55aec93 100644
--- a/solr/solr-ref-guide/src/machine-learning.adoc
+++ b/solr/solr-ref-guide/src/machine-learning.adoc
@@ -348,7 +348,7 @@ r=knnRegress(obs, quality, 5, scale="true"),
 The `knnSearch` function returns the k-nearest neighbors
 for a document based on text similarity.
 Under the covers the `knnSearch` function
-uses the More Like This query parser plugin. This capability uses the search
+uses Solr's More Like This query parser plugin. This capability uses the search
 engines query, term statistics, scoring and ranking capability to perform a fast,
 nearest neighbor search for similar documents over large distributed indexes.
 
@@ -356,13 +356,11 @@ The results of this
 search can be used directly or provide *candidates* for machine learning operations such
 as a secondary knn vector search.
 
-The example below shows the `knnSearch` function run over a movie reviews data set. The
-search returns the 50 documents most similar to document id *83e9b5b0-...* based on
-the similarity of the *review_t* field which contains
-the text of the review. The *mindf* and *maxdf* specify the min and max document frequency of the terms
-used to perform the search. This makes the query faster by eliminating very high frequency terms
-and terms and also improves accuracy be removing noise from search.
-
+The example below shows the `knnSearch` function on a movie reviews data set. The
+search returns the 50 documents most similar to a specific document id (*83e9b5b0...*) based on
+the similarity of the *review_t* field. The *mindf* and *maxdf* specify the min and max document frequency of the terms
+used to perform the search. These parameters can make the query faster by eliminating
+very high frequency terms and also improves accuracy be removing noise from search.
 
 image::images/math-expressions/knnSearch.png[]