[madlib] branch master updated: indicate optional params in DR and RF

[fm...@apache.org]

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fmcquillan pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/madlib.git


The following commit(s) were added to refs/heads/master by this push:
     new ac30a3c  indicate optional params in DR and RF
ac30a3c is described below

commit ac30a3c508509a6996f872b0a7505b215c94fd85
Author: Frank McQuillan <fm...@pivotal.io>
AuthorDate: Wed Feb 12 17:06:19 2020 -0800

    indicate optional params in DR and RF
---
 .../postgres/modules/recursive_partitioning/decision_tree.sql_in    | 6 +++---
 .../postgres/modules/recursive_partitioning/random_forest.sql_in    | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in b/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in
index 2408770..04f7b82 100644
--- a/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in
+++ b/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in
@@ -537,7 +537,7 @@ tree_predict(tree_model,
   'estimated_prob_<em>dep_value</em>', where <em>dep_value</em> represents each
   value of the response variable.</DD>
 
-  <DT>type</DT>
+  <DT>type (optional)</DT>
   <DD>TEXT, optional, default: 'response'. For regression trees, the output is
       always the predicted value of the dependent variable. For classification
       trees, the <em>type</em> variable can be 'response', giving the
@@ -580,10 +580,10 @@ split for a tuple.
 <DL class="arglist">
     <DT>tree_model</DT>
     <DD>TEXT. Name of the table containing the decision tree model.</DD>
-    <DT>dot_format</DT>
+    <DT>dot_format (optional)</DT>
     <DD>BOOLEAN, default = TRUE. Output can either be in a dot format or a text
     format. If TRUE, the result is in the dot format, else output is in text format.</DD>
-    <DT>verbosity</DT>
+    <DT>verbosity (optional)</DT>
     <DD>BOOLEAN, default = FALSE. If set to TRUE, the dot format output will contain
     additional information (impurity, sample size, number of weighted rows
     for each response variable, classification or prediction if the tree
diff --git a/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in b/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in
index 251dfbc..888388c 100644
--- a/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in
+++ b/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in
@@ -545,7 +545,7 @@ forest_predict(random_forest_model,
   'estimated_prob_<em>dep_value</em>', where <em>dep_value</em> represents each
   value of the response variable.</DD>
 
-  <DT>type</DT>
+  <DT>type (optional)</DT>
   <DD>TEXT, optional, default: 'response'. For regression trees, the output is
       always the predicted value of the dependent variable. For classification
       trees, the <em>type</em> variable can be 'response', giving the