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@@ -2500,7 +2500,7 @@ Apache Hivemall is an effort undergoing incubation at The 
Apache Software Founda
         
     
         
-        <script 
src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/3.1.1/anchor.min.js";></script>
+        <script 
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/binaryclass/news20_rf.html
----------------------------------------------------------------------
diff --git a/userguide/binaryclass/news20_rf.html 
b/userguide/binaryclass/news20_rf.html
index 8d5b3a4..d328203 100644
--- a/userguide/binaryclass/news20_rf.html
+++ b/userguide/binaryclass/news20_rf.html
@@ -2216,7 +2216,7 @@
   under the License.
 -->
 <p>Hivemall Random Forest supports libsvm-like sparse inputs. </p>
-<div class="panel panel-primary"><div class="panel-heading"><h3 
class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div 
class="panel-body"><p>This feature, i.e., Sparse input support in Random 
Forest, is supported since Hivemall v0.5-rc.1 or later._
+<div class="panel panel-primary"><div class="panel-heading"><h3 
class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div 
class="panel-body"><p>This feature, i.e., Sparse input support in Random 
Forest, is supported since Hivemall v0.5.0 or later._
 <a 
href="http://hivemall.incubator.apache.org/userguide/ft_engineering/hashing.html#featurehashing-function";
 target="_blank"><code>feature_hashing</code></a> function is useful to prepare 
feature vectors for Random Forest.</p></div></div>
 <!-- toc --><div id="toc" class="toc">
 
@@ -2242,7 +2242,7 @@
 </code></pre>
 <div class="panel panel-warning"><div class="panel-heading"><h3 
class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> 
Caution</h3></div><div class="panel-body"><p>label must be in <code>[0, 
k)</code> where <code>k</code> is the number of classes.</p></div></div>
 <h2 id="prediction">Prediction</h2>
-<pre><code class="lang-sql"><span class="hljs-keyword">SET</span> 
hivevar:classification=<span class="hljs-literal">true</span>;
+<pre><code class="lang-sql"><span class="hljs-comment">-- SET 
hivevar:classification=true;</span>
 
 <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> 
rf_predicted;
 <span class="hljs-keyword">create</span> <span 
class="hljs-keyword">table</span> rf_predicted
@@ -2255,7 +2255,10 @@
   <span class="hljs-keyword">SELECT</span>
     <span class="hljs-keyword">rowid</span>, 
     m.model_weight,
-    tree_predict(m.model_id, m.<span class="hljs-keyword">model</span>, 
t.features, ${classification}) <span class="hljs-keyword">as</span> predicted
+    <span class="hljs-comment">-- v0.5.0 and later</span>
+    tree_predict(m.model_id, m.<span class="hljs-keyword">model</span>, 
t.features, <span class="hljs-string">&quot;-classification&quot;</span>) <span 
class="hljs-keyword">as</span> predicted
+    <span class="hljs-comment">-- before v0.5.0</span>
+    <span class="hljs-comment">-- tree_predict(m.model_id, m.model, 
t.features, ${classification}) as predicted</span>
   <span class="hljs-keyword">FROM</span>
     rf_model m
     <span class="hljs-keyword">LEFT</span> <span 
class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> <span 
class="hljs-comment">-- CROSS JOIN</span>
@@ -2336,7 +2339,7 @@ Apache Hivemall is an effort undergoing incubation at The 
Apache Software Founda
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@@ -2366,7 +2369,7 @@ Apache Hivemall is an effort undergoing incubation at The 
Apache Software Founda
         
     
         
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/binaryclass/news20_scw.html
----------------------------------------------------------------------
diff --git a/userguide/binaryclass/news20_scw.html 
b/userguide/binaryclass/news20_scw.html
index 8e39cf3..0985798 100644
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@@ -2529,7 +2529,7 @@ Apache Hivemall is an effort undergoing incubation at The 
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@@ -2559,7 +2559,7 @@ Apache Hivemall is an effort undergoing incubation at The 
Apache Software Founda
         
     
         
-        <script 
src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/3.1.1/anchor.min.js";></script>
+        <script 
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/binaryclass/titanic_rf.html
----------------------------------------------------------------------
diff --git a/userguide/binaryclass/titanic_rf.html 
b/userguide/binaryclass/titanic_rf.html
index 87a2bb8..f7016a2 100644
--- a/userguide/binaryclass/titanic_rf.html
+++ b/userguide/binaryclass/titanic_rf.html
@@ -2344,8 +2344,8 @@ awk <span class="hljs-string">&apos;{ 
FPAT=&quot;([^,]*)|(\&quot;[^\&quot;]+\&qu
 <p>Q,C,C,Q,Q,Q,C,Q,C,C</p>
 </blockquote>
 <p><code>Q</code> and <code>C</code> represent quantitative variable and 
categorical variables, respectively.</p>
-<p><em>Caution:</em> Note that the output of 
<code>guess_attribute_types</code> is not perfect. Revise it by your self.
-For example, <code>pclass</code> is a categorical variable.</p>
+<div class="panel panel-warning"><div class="panel-heading"><h3 
class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> 
Caution</h3></div><div class="panel-body"><p>Note that the output of 
<code>guess_attribute_types</code> is not perfect. Revise it by your self.
+For example, <code>pclass</code> is a categorical variable.</p></div></div>
 <pre><code class="lang-sql"><span class="hljs-keyword">set</span> 
hivevar:attrs=C,C,C,Q,Q,Q,C,Q,C,C;
 
 <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> 
model_rf;
@@ -2353,7 +2353,6 @@ For example, <code>pclass</code> is a categorical 
variable.</p>
 <span class="hljs-keyword">AS</span>
 <span class="hljs-keyword">select</span>
   train_randomforest_classifier(features, survived, <span 
class="hljs-string">&quot;-trees 500 -attrs ${attrs}&quot;</span>) 
-    <span class="hljs-comment">-- as (model_id, model_type, pred_model, 
var_importance, oob_errors, oob_tests)</span>
 <span class="hljs-keyword">from</span>
   train_rf
 ;
@@ -2368,7 +2367,7 @@ For example, <code>pclass</code> is a categorical 
variable.</p>
 
<p>[137.00242639169272,1194.2140119834373,328.78017188176966,628.2568660509628,200.31275032394072,160.12876797647078,1083.5987543408116,664.1234312561456,422.89449844090393,130.72019667694784]
     0.18742985409652077</p>
 </blockquote>
 <h1 id="prediction">Prediction</h1>
-<pre><code class="lang-sql"><span class="hljs-keyword">SET</span> 
hivevar:classification=<span class="hljs-literal">true</span>;
+<pre><code class="lang-sql"><span class="hljs-comment">-- SET 
hivevar:classification=true;</span>
 <span class="hljs-keyword">set</span> hive.<span 
class="hljs-keyword">auto</span>.<span 
class="hljs-keyword">convert</span>.<span 
class="hljs-keyword">join</span>=<span class="hljs-literal">true</span>;
 <span class="hljs-keyword">SET</span> hive.mapjoin.optimized.hashtable=<span 
class="hljs-literal">false</span>;
 <span class="hljs-keyword">SET</span> mapred.reduce.tasks=<span 
class="hljs-number">16</span>;
@@ -2385,23 +2384,23 @@ For example, <code>pclass</code> is a categorical 
variable.</p>
   <span class="hljs-keyword">SELECT</span>
     passengerid,
     <span class="hljs-comment">-- rf_ensemble(predicted) as predicted</span>
-    <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span>
+    <span class="hljs-comment">-- v0.5.0 or later</span>
     rf_ensemble(predicted.<span class="hljs-keyword">value</span>, 
predicted.posteriori, model_weight) <span class="hljs-keyword">as</span> 
predicted
     <span class="hljs-comment">-- rf_ensemble(predicted.value, 
predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., 
model_weight)</span>
   <span class="hljs-keyword">FROM</span> (
     <span class="hljs-keyword">SELECT</span>
       t.passengerid, 
-      <span class="hljs-comment">-- hivemall v0.4.1-alpha.3 or later</span>
+      <span class="hljs-comment">-- from v0.4.1-alpha.3 to v0.4.2-rc4</span>
       <span class="hljs-comment">-- tree_predict(p.model_id, p.model_type, 
p.pred_model, t.features, ${classification}) as predicted</span>
-      <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span>
+      <span class="hljs-comment">-- v0.5.0 or later</span>
       p.model_weight,
-      tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, 
t.features, ${classification}) <span class="hljs-keyword">as</span> predicted
-      <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, 
p.pred_model, t.features, ${classification}) as predicted -- to use the old 
model in v0.5-rc.1 or later</span>
+      tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, 
t.features, <span class="hljs-string">&quot;-classification&quot;</span>) <span 
class="hljs-keyword">as</span> predicted
+      <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, 
p.pred_model, t.features, ${classification}) as predicted -- to use the old 
model in v0.5.0 or later</span>
     <span class="hljs-keyword">FROM</span> (
       <span class="hljs-keyword">SELECT</span> 
-        <span class="hljs-comment">-- hivemall v0.4.1-alpha.3 or later</span>
+        <span class="hljs-comment">-- from v0.4.1-alpha.3 or v0.4.2-rc4</span>
         <span class="hljs-comment">-- model_id, model_type, pred_model</span>
-        <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span>
+        <span class="hljs-comment">-- v0.5.0 or later</span>
         model_id, model_weight, <span class="hljs-keyword">model</span>
       <span class="hljs-keyword">FROM</span> 
         model_rf 
@@ -2414,7 +2413,7 @@ For example, <code>pclass</code> is a categorical 
variable.</p>
 ) t2
 ;
 </code></pre>
-<div class="panel panel-warning"><div class="panel-heading"><h3 
class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> 
Caution</h3></div><div class="panel-body"><p><code>tree_predict_v1</code> is 
for the backward compatibility for using prediction models built before 
<code>v0.5-rc.1</code> on <code>v0.5-rc.1</code> or later.</p></div></div>
+<div class="panel panel-warning"><div class="panel-heading"><h3 
class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> 
Caution</h3></div><div class="panel-body"><p><code>tree_predict_v1</code> is 
for the backward compatibility for using prediction models built before 
<code>v0.5.0</code> on <code>v0.5.0</code> or later.</p></div></div>
 <h1 id="kaggle-submission">Kaggle submission</h1>
 <pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span 
class="hljs-keyword">table</span> predicted_rf_submit;
 <span class="hljs-keyword">create</span> <span 
class="hljs-keyword">table</span> predicted_rf_submit
@@ -2432,7 +2431,7 @@ sed -i <span class="hljs-_">-e</span> <span 
class="hljs-string">&quot;1i Passeng
 </code></pre>
 <p>Accuracy would gives <code>0.76555</code> for a Kaggle submission.</p>
 <h1 id="graphvis-export">Graphvis export</h1>
-<div class="panel panel-primary"><div class="panel-heading"><h3 
class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div 
class="panel-body"><p><code>tree_export</code> feature is supported from 
Hivemall v0.5-rc.1 or later.
+<div class="panel panel-primary"><div class="panel-heading"><h3 
class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div 
class="panel-body"><p><code>tree_export</code> feature is supported from 
Hivemall v0.5.0 or later.
 Better to limit tree depth on training by <code>-depth</code> option to plot a 
Decision Tree.</p></div></div>
 <p>Hivemall provide <code>tree_export</code> to export a decision tree into <a 
href="http://www.graphviz.org/"; target="_blank">Graphviz</a> or human-readable 
Javascript format. You can find the usage by issuing the following query:</p>
 <pre><code>&gt; select tree_export(&quot;&quot;,&quot;-help&quot;);
@@ -2490,7 +2489,7 @@ usage: tree_export(string model, const string options, 
optional
 <blockquote>
 
<p>[116.12055542977338,960.8569891444097,291.08765260103837,469.74671636586226,163.721292772701,120.784769882858,847.9769298113661,554.4617571355476,346.3500941757221,97.42593940113392]
    0.1838351822503962</p>
 </blockquote>
-<pre><code class="lang-sql"><span class="hljs-keyword">SET</span> 
hivevar:classification=<span class="hljs-literal">true</span>;
+<pre><code class="lang-sql"><span class="hljs-comment">-- SET 
hivevar:classification=true;</span>
 <span class="hljs-keyword">SET</span> hive.mapjoin.optimized.hashtable=<span 
class="hljs-literal">false</span>;
 <span class="hljs-keyword">SET</span> mapred.reduce.tasks=<span 
class="hljs-number">16</span>;
 
@@ -2506,23 +2505,24 @@ usage: tree_export(string model, const string options, 
optional
   <span class="hljs-keyword">SELECT</span>
     passengerid,
     <span class="hljs-comment">-- rf_ensemble(predicted) as predicted</span>
-    <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span>
+    <span class="hljs-comment">-- v0.5.0 or later</span>
     rf_ensemble(predicted.<span class="hljs-keyword">value</span>, 
predicted.posteriori, model_weight) <span class="hljs-keyword">as</span> 
predicted
     <span class="hljs-comment">-- rf_ensemble(predicted.value, 
predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., 
model_weight)</span>
   <span class="hljs-keyword">FROM</span> (
     <span class="hljs-keyword">SELECT</span>
       t.passengerid, 
-      <span class="hljs-comment">-- hivemall v0.4.1-alpha.3 or later</span>
+      <span class="hljs-comment">-- from v0.4.1-alpha.3 or v0.4.2-rc4</span>
       <span class="hljs-comment">-- tree_predict(p.model_id, p.model_type, 
p.pred_model, t.features, ${classification}) as predicted</span>
-      <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span>
+      <span class="hljs-comment">-- v0.5.0 or later</span>
       p.model_weight,
-      tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, 
t.features, ${classification}) <span class="hljs-keyword">as</span> predicted
-      <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, 
p.pred_model, t.features, ${classification}) as predicted -- to use the old 
model in v0.5-rc.1 or later</span>
+      tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, 
t.features, <span class="hljs-string">&quot;-classification&quot;</span>) <span 
class="hljs-keyword">as</span> predicted
+      <span class="hljs-comment">-- tree_predict(p.model_id, p.model, 
t.features, ${classification}) as predicted</span>
+      <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, 
p.pred_model, t.features, ${classification}) as predicted -- to use the old 
model in v0.5.0 or later</span>
     <span class="hljs-keyword">FROM</span> (
       <span class="hljs-keyword">SELECT</span> 
-        <span class="hljs-comment">-- hivemall v0.4.1-alpha.3 or later</span>
+        <span class="hljs-comment">-- from v0.4.1-alpha.3 to v0.4.2-rc4</span>
         <span class="hljs-comment">-- model_id, model_type, pred_model</span>
-        <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span>
+        <span class="hljs-comment">-- v0.5.0 or later</span>
         model_id, model_weight, <span class="hljs-keyword">model</span>
       <span class="hljs-keyword">FROM</span> 
         model_rf_07
@@ -2613,7 +2613,7 @@ Apache Hivemall is an effort undergoing incubation at The 
Apache Software Founda
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/binaryclass/webspam.html
----------------------------------------------------------------------
diff --git a/userguide/binaryclass/webspam.html 
b/userguide/binaryclass/webspam.html
index 9a37da8..c913479 100644
--- a/userguide/binaryclass/webspam.html
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