[11/23] incubator-hivemall-site git commit: Updated userguide for evaluation section

[my...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/a98b42f8/userguide/misc/prediction.html
----------------------------------------------------------------------
diff --git a/userguide/misc/prediction.html b/userguide/misc/prediction.html
index fae3594..60732ee 100644
--- a/userguide/misc/prediction.html
+++ b/userguide/misc/prediction.html
@@ -244,7 +244,7 @@
                     
                         <b>1.3.1.</b>
                     
-                    Explicit addBias() for better prediction
+                    Explicit add_bias() for better prediction
             
                 </a>
             
@@ -707,14 +707,14 @@
         
         
     
-        <li class="chapter " data-level="4.1" data-path="../eval/stat_eval.html">
+        <li class="chapter " data-level="4.1" data-path="../eval/binary_classification_measures.html">
             
-                <a href="../eval/stat_eval.html">
+                <a href="../eval/binary_classification_measures.html">
             
                     
                         <b>4.1.</b>
                     
-                    Statistical evaluation of a prediction model
+                    Binary Classification Metrics
             
                 </a>
             
@@ -743,13 +743,43 @@
             
         </li>
     
-        <li class="chapter " data-level="4.2" data-path="../eval/rank.html">
+        <li class="chapter " data-level="4.2" data-path="../eval/multilabel_classification_measures.html">
             
-                <a href="../eval/rank.html">
+                <a href="../eval/multilabel_classification_measures.html">
             
                     
                         <b>4.2.</b>
                     
+                    Multi-label Classification Metrics
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="4.3" data-path="../eval/regression.html">
+            
+                <a href="../eval/regression.html">
+            
+                    
+                        <b>4.3.</b>
+                    
+                    Regression metrics
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="4.4" data-path="../eval/rank.html">
+            
+                <a href="../eval/rank.html">
+            
+                    
+                        <b>4.4.</b>
+                    
                     Ranking Measures
             
                 </a>
@@ -758,12 +788,12 @@
             
         </li>
     
-        <li class="chapter " data-level="4.3" data-path="../eval/datagen.html">
+        <li class="chapter " data-level="4.5" data-path="../eval/datagen.html">
             
                 <a href="../eval/datagen.html">
             
                     
-                        <b>4.3.</b>
+                        <b>4.5.</b>
                     
                     Data Generation
             
@@ -774,12 +804,12 @@
             <ul class="articles">
                 
     
-        <li class="chapter " data-level="4.3.1" data-path="../eval/lr_datagen.html">
+        <li class="chapter " data-level="4.5.1" data-path="../eval/lr_datagen.html">
             
                 <a href="../eval/lr_datagen.html">
             
                     
-                        <b>4.3.1.</b>
+                        <b>4.5.1.</b>
                     
                     Logistic Regression data generation
             
@@ -2219,7 +2249,7 @@
 </tbody>
 </table>
 <p>In practice, target values could be any of small/large float/int negative/positive values. <a href="../regression/kddcup12tr2.html">Our CTR prediction tutorial</a> solves regression problem with small floating point target values in a 0-1 range, for example.</p>
-<p>While there are several ways to realize regression by using Hivemall, <code>train_regression()</code> is one of the most flexible functions. This feature is explained in: <a href="../regression/general.html">Regression</a>.</p>
+<p>While there are several ways to realize regression by using Hivemall, <code>train_regressor()</code> is one of the most flexible functions. This feature is explained in <a href="../regression/general.html">this page</a>.</p>
 <h1 id="classification">Classification</h1>
 <p>In contrast to regression, output for classification problems should be (integer) <strong>labels</strong>:</p>
 <table>
@@ -2257,14 +2287,14 @@
 <li><strong>Input:</strong> a vector <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mrow><mi mathvariant="bold">x</mi></mrow></mrow><annotation encoding="application/x-tex">\mathbf{x}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.44444em;"></span><span class="strut bottom" style="height:0.44444em;vertical-align:0em;"></span><span class="base textstyle uncramped"><span class="mord textstyle uncramped"><span class="mord mathbf">x</span></span></span></span></span></li>
 <li><strong>Output:</strong> a value <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.43056em;"></span><span class="strut bottom" style="height:0.625em;vertical-align:-0.19444em;"></span><span class="base textstyle uncramped"><span class="mord mathit" style="margin-right:0.03588em;">y</span></span></span></span></li>
 </ul>
-<p>For a set of samples <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo>(</mo><msub><mrow><mi mathvariant="bold">x</mi></mrow><mn>1</mn></msub><mo separator="true">,</mo><msub><mi>y</mi><mn>1</mn></msub><mo>)</mo><mo separator="true">,</mo><mo>(</mo><msub><mrow><mi mathvariant="bold">x</mi></mrow><mn>2</mn></msub><mo separator="true">,</mo><msub><mi>y</mi><mn>2</mn></msub><mo>)</mo><mo separator="true">,</mo><mo>&#x22EF;</mo><mo separator="true">,</mo><mo>(</mo><msub><mrow><mi mathvariant="bold">x</mi></mrow><mi>n</mi></msub><mo separator="true">,</mo><msub><mi>y</mi><mi>n</mi></msub><mo>)</mo></mrow><annotation encoding="application/x-tex">(\mathbf{x}_1, y_1), (\mathbf{x}_2, y_2), \cdots, (\mathbf{x}_n, y_n)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.75em;"></span><span class="strut bottom" style="height:1em;vertical-align:-0.25em;"></span><span class="base textstyle uncramped"><spa
 n class="mopen">(</span><span class=""><span class="mord textstyle uncramped"><span class="mord mathbf">x</span></span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord mathrm">1</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mpunct">,</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">y</span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord mathrm">1</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer 
 reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mclose">)</span><span class="mpunct">,</span><span class="mopen">(</span><span class=""><span class="mord textstyle uncramped"><span class="mord mathbf">x</span></span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord mathrm">2</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mpunct">,</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">y</span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span><
 /span><span class="reset-textstyle scriptstyle cramped"><span class="mord mathrm">2</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mclose">)</span><span class="mpunct">,</span><span class="minner">&#x22EF;</span><span class="mpunct">,</span><span class="mopen">(</span><span class=""><span class="mord textstyle uncramped"><span class="mord mathbf">x</span></span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord mathit">n</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mpunct">,</span><span class="mord"><span class="mord mathi
 t" style="margin-right:0.03588em;">y</span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord mathit">n</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mclose">)</span></span></span></span>, the goal of prediction algorithms is to find a weight vector (i.e., parameters) <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mrow><mi mathvariant="bold">w</mi></mrow></mrow><annotation encoding="application/x-tex">\mathbf{w}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.44444em;"></span><span class="strut bottom" style="height:0.44444em;vertical-align:0em;"><
 /span><span class="base textstyle uncramped"><span class="mord textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span></span></span></span> by minimizing the following error:</p>
+<p>For a set of samples <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo>(</mo><msub><mrow><mi mathvariant="bold">x</mi></mrow><mn>1</mn></msub><mo separator="true">,</mo><msub><mi>y</mi><mn>1</mn></msub><mo>)</mo><mo separator="true">,</mo><mo>(</mo><msub><mrow><mi mathvariant="bold">x</mi></mrow><mn>2</mn></msub><mo separator="true">,</mo><msub><mi>y</mi><mn>2</mn></msub><mo>)</mo><mo separator="true">,</mo><mo>&#x22EF;</mo><mo separator="true">,</mo><mo>(</mo><msub><mrow><mi mathvariant="bold">x</mi></mrow><mi>n</mi></msub><mo separator="true">,</mo><msub><mi>y</mi><mi>n</mi></msub><mo>)</mo></mrow><annotation encoding="application/x-tex">(\mathbf{x}_1, y_1), (\mathbf{x}_2, y_2), \cdots, (\mathbf{x}_n, y_n)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.75em;"></span><span class="strut bottom" style="height:1em;vertical-align:-0.25em;"></span><span class="base textstyle uncramped"><spa
 n class="mopen">(</span><span class="mord"><span class="mord textstyle uncramped"><span class="mord mathbf">x</span></span><span class="msupsub"><span class="vlist"><span style="top:0.15em;margin-right:0.05em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord mathrm mtight">1</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span></span><span class="mpunct">,</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord mathrm mtig
 ht">1</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span></span><span class="mclose">)</span><span class="mpunct">,</span><span class="mopen">(</span><span class="mord"><span class="mord textstyle uncramped"><span class="mord mathbf">x</span></span><span class="msupsub"><span class="vlist"><span style="top:0.15em;margin-right:0.05em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord mathrm mtight">2</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span></span><span class="mpunct">,</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist"><spa
 n style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord mathrm mtight">2</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span></span><span class="mclose">)</span><span class="mpunct">,</span><span class="minner">&#x22EF;</span><span class="mpunct">,</span><span class="mopen">(</span><span class="mord"><span class="mord textstyle uncramped"><span class="mord mathbf">x</span></span><span class="msupsub"><span class="vlist"><span style="top:0.15em;margin-right:0.05em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord mathit mtight">n</span></span></span><span c
 lass="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span></span><span class="mpunct">,</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord mathit mtight">n</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span></span><span class="mclose">)</span></span></span></span>, the goal of prediction algorithms is to find a weight vector (i.e., parameters) <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mrow><mi mathvariant="bold">w</mi></mr
 ow></mrow><annotation encoding="application/x-tex">\mathbf{w}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.44444em;"></span><span class="strut bottom" style="height:0.44444em;vertical-align:0em;"></span><span class="base textstyle uncramped"><span class="mord textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span></span></span></span> by minimizing the following error:</p>
 <p><span class="katex-display"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi>E</mi><mo>(</mo><mrow><mi mathvariant="bold">w</mi></mrow><mo>)</mo><mo>:</mo><mo>=</mo><mfrac><mrow><mn>1</mn></mrow><mrow><mi>n</mi></mrow></mfrac><msubsup><mo>&#x2211;</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mrow><mi>n</mi></mrow></msubsup><mi>L</mi><mo>(</mo><mrow><mi mathvariant="bold">w</mi></mrow><mo separator="true">;</mo><msub><mrow><mi mathvariant="bold">x</mi></mrow><mi>i</mi></msub><mo separator="true">,</mo><msub><mi>y</mi><mi>i</mi></msub><mo>)</mo><mo>+</mo><mi>&#x3BB;</mi><mi>R</mi><mo>(</mo><mrow><mi mathvariant="bold">w</mi></mrow><mo>)</mo></mrow><annotation encoding="application/x-tex">
 E(\mathbf{w}) := \frac{1}{n} \sum_{i=1}^{n} L(\mathbf{w}; \mathbf{x}_i, y_i) + \lambda R(\mathbf{w})
-</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:1.6513970000000002em;"></span><span class="strut bottom" style="height:2.929066em;vertical-align:-1.277669em;"></span><span class="base displaystyle textstyle uncramped"><span class="mord mathit" style="margin-right:0.05764em;">E</span><span class="mopen">(</span><span class="mord displaystyle textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span><span class="mclose">)</span><span class="mrel">:</span><span class="mrel">=</span><span class="mord reset-textstyle displaystyle textstyle uncramped"><span class="sizing reset-size5 size5 reset-textstyle textstyle uncramped nulldelimiter"></span><span class="mfrac"><span class="vlist"><span style="top:0.686em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle textstyle cramped"><span class="mord textstyle c
 ramped"><span class="mord mathit">n</span></span></span></span><span style="top:-0.22999999999999998em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle textstyle uncramped frac-line"></span></span><span style="top:-0.677em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle textstyle uncramped"><span class="mord textstyle uncramped"><span class="mord mathrm">1</span></span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="sizing reset-size5 size5 reset-textstyle textstyle uncramped nulldelimiter"></span></span><span class="mop op-limits"><span class="vlist"><span style="top:1.1776689999999999em;margin-left:0em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x20
 0B;</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord scriptstyle cramped"><span class="mord mathit">i</span><span class="mrel">=</span><span class="mord mathrm">1</span></span></span></span><span style="top:-0.000005000000000143778em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span><span class="op-symbol large-op mop">&#x2211;</span></span></span><span style="top:-1.2500050000000003em;margin-left:0em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle uncramped"><span class="mord scriptstyle uncramped"><span class="mord mathit">n</span></span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mord mathit">L</span><span class="mopen">(</span><span class="mord displaystyle tex
 tstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span><span class="mpunct">;</span><span class=""><span class="mord displaystyle textstyle uncramped"><span class="mord mathbf">x</span></span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord mathit">i</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mpunct">,</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">y</span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramp
 ed"><span class="mord mathit">i</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mclose">)</span><span class="mbin">+</span><span class="mord mathit">&#x3BB;</span><span class="mord mathit" style="margin-right:0.00773em;">R</span><span class="mopen">(</span><span class="mord displaystyle textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span><span class="mclose">)</span></span></span></span></span></p>
+</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:1.6513970000000002em;"></span><span class="strut bottom" style="height:2.929066em;vertical-align:-1.277669em;"></span><span class="base displaystyle textstyle uncramped"><span class="mord mathit" style="margin-right:0.05764em;">E</span><span class="mopen">(</span><span class="mord displaystyle textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span><span class="mclose">)</span><span class="mrel">:</span><span class="mrel">=</span><span class="mord reset-textstyle displaystyle textstyle uncramped"><span class="mopen sizing reset-size5 size5 reset-textstyle textstyle uncramped nulldelimiter"></span><span class="mfrac"><span class="vlist"><span style="top:0.686em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle textstyle cramped"><span class="mord texts
 tyle cramped"><span class="mord mathit">n</span></span></span></span><span style="top:-0.22999999999999998em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle textstyle uncramped frac-line"></span></span><span style="top:-0.677em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle textstyle uncramped"><span class="mord textstyle uncramped"><span class="mord mathrm">1</span></span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mclose sizing reset-size5 size5 reset-textstyle textstyle uncramped nulldelimiter"></span></span><span class="mop op-limits"><span class="vlist"><span style="top:1.1776689999999999em;margin-left:0em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-siz
 e:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord scriptstyle cramped mtight"><span class="mord mathit mtight">i</span><span class="mrel mtight">=</span><span class="mord mathrm mtight">1</span></span></span></span><span style="top:-0.000005000000000143778em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span><span class="mop op-symbol large-op">&#x2211;</span></span></span><span style="top:-1.2500050000000003em;margin-left:0em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle uncramped mtight"><span class="mord scriptstyle uncramped mtight"><span class="mord mathit mtight">n</span></span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mord mathit">L
 </span><span class="mopen">(</span><span class="mord displaystyle textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span><span class="mpunct">;</span><span class="mord"><span class="mord displaystyle textstyle uncramped"><span class="mord mathbf">x</span></span><span class="msupsub"><span class="vlist"><span style="top:0.15em;margin-right:0.05em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord mathit mtight">i</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span></span><span class="mpunct">,</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span cl
 ass="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord mathit mtight">i</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span></span><span class="mclose">)</span><span class="mbin">+</span><span class="mord mathit">&#x3BB;</span><span class="mord mathit" style="margin-right:0.00773em;">R</span><span class="mopen">(</span><span class="mord displaystyle textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span><span class="mclose">)</span></span></span></span></span></p>
 <p>In the above formulation, there are two auxiliary functions we have to know: </p>
 <ul>
-<li><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi>L</mi><mo>(</mo><mrow><mi mathvariant="bold">w</mi></mrow><mo separator="true">;</mo><msub><mrow><mi mathvariant="bold">x</mi></mrow><mi>i</mi></msub><mo separator="true">,</mo><msub><mi>y</mi><mi>i</mi></msub><mo>)</mo></mrow><annotation encoding="application/x-tex">L(\mathbf{w}; \mathbf{x}_i, y_i)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.75em;"></span><span class="strut bottom" style="height:1em;vertical-align:-0.25em;"></span><span class="base textstyle uncramped"><span class="mord mathit">L</span><span class="mopen">(</span><span class="mord textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span><span class="mpunct">;</span><span class=""><span class="mord textstyle uncramped"><span class="mord mathbf">x</span></span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;"><span 
 class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord mathit">i</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mpunct">,</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">y</span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord mathit">i</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mclose">)</span></span></span></span><ul>
-<li><strong>Loss function</strong> for a single sample <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo>(</mo><msub><mrow><mi mathvariant="bold">x</mi></mrow><mi>i</mi></msub><mo separator="true">,</mo><msub><mi>y</mi><mi>i</mi></msub><mo>)</mo></mrow><annotation encoding="application/x-tex">(\mathbf{x}_i, y_i)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.75em;"></span><span class="strut bottom" style="height:1em;vertical-align:-0.25em;"></span><span class="base textstyle uncramped"><span class="mopen">(</span><span class=""><span class="mord textstyle uncramped"><span class="mord mathbf">x</span></span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord mathit">i</span></span></span><span class="baseline-fi
 x"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mpunct">,</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">y</span><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped"><span class="mord mathit">i</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span><span class="mclose">)</span></span></span></span> and given <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mrow><mi mathvariant="bold">w</mi></mrow></mrow><annotation encoding="application/x-tex">\mathbf{w}</annotation></semantics></math></span><span class="katex-html" aria-hidden="
 true"><span class="strut" style="height:0.44444em;"></span><span class="strut bottom" style="height:0.44444em;vertical-align:0em;"></span><span class="base textstyle uncramped"><span class="mord textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span></span></span></span>.</li>
+<li><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi>L</mi><mo>(</mo><mrow><mi mathvariant="bold">w</mi></mrow><mo separator="true">;</mo><msub><mrow><mi mathvariant="bold">x</mi></mrow><mi>i</mi></msub><mo separator="true">,</mo><msub><mi>y</mi><mi>i</mi></msub><mo>)</mo></mrow><annotation encoding="application/x-tex">L(\mathbf{w}; \mathbf{x}_i, y_i)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.75em;"></span><span class="strut bottom" style="height:1em;vertical-align:-0.25em;"></span><span class="base textstyle uncramped"><span class="mord mathit">L</span><span class="mopen">(</span><span class="mord textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span><span class="mpunct">;</span><span class="mord"><span class="mord textstyle uncramped"><span class="mord mathbf">x</span></span><span class="msupsub"><span class="vlist"><span style="top:0.15em;ma
 rgin-right:0.05em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord mathit mtight">i</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span></span><span class="mpunct">,</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord mathit mtight">i</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span></span><s
 pan class="mclose">)</span></span></span></span><ul>
+<li><strong>Loss function</strong> for a single sample <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo>(</mo><msub><mrow><mi mathvariant="bold">x</mi></mrow><mi>i</mi></msub><mo separator="true">,</mo><msub><mi>y</mi><mi>i</mi></msub><mo>)</mo></mrow><annotation encoding="application/x-tex">(\mathbf{x}_i, y_i)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.75em;"></span><span class="strut bottom" style="height:1em;vertical-align:-0.25em;"></span><span class="base textstyle uncramped"><span class="mopen">(</span><span class="mord"><span class="mord textstyle uncramped"><span class="mord mathbf">x</span></span><span class="msupsub"><span class="vlist"><span style="top:0.15em;margin-right:0.05em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord mathit mtight">i</spa
 n></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span></span><span class="mpunct">,</span><span class="mord"><span class="mord mathit" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist"><span style="top:0.15em;margin-right:0.05em;margin-left:-0.03588em;"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span><span class="reset-textstyle scriptstyle cramped mtight"><span class="mord mathit mtight">i</span></span></span><span class="baseline-fix"><span class="fontsize-ensurer reset-size5 size5"><span style="font-size:0em;">&#x200B;</span></span>&#x200B;</span></span></span></span><span class="mclose">)</span></span></span></span> and given <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mrow><mi mathvariant="bold">w</mi></mrow></mrow><annotation encoding="application/x-te
 x">\mathbf{w}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.44444em;"></span><span class="strut bottom" style="height:0.44444em;vertical-align:0em;"></span><span class="base textstyle uncramped"><span class="mord textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span></span></span></span>.</li>
 <li>If this function produces small values, it means the parameter <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mrow><mi mathvariant="bold">w</mi></mrow></mrow><annotation encoding="application/x-tex">\mathbf{w}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.44444em;"></span><span class="strut bottom" style="height:0.44444em;vertical-align:0em;"></span><span class="base textstyle uncramped"><span class="mord textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span></span></span></span> is successfully learnt. </li>
 </ul>
 </li>
@@ -2277,15 +2307,15 @@ E(\mathbf{w}) := \frac{1}{n} \sum_{i=1}^{n} L(\mathbf{w}; \mathbf{x}_i, y_i) + \
 <p>(<span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi>&#x3BB;</mi></mrow><annotation encoding="application/x-tex">\lambda</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.69444em;"></span><span class="strut bottom" style="height:0.69444em;vertical-align:0em;"></span><span class="base textstyle uncramped"><span class="mord mathit">&#x3BB;</span></span></span></span> is a small value which controls the effect of regularization function.)</p>
 <p>Eventually, minimizing the function <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi>E</mi><mo>(</mo><mrow><mi mathvariant="bold">w</mi></mrow><mo>)</mo></mrow><annotation encoding="application/x-tex">E(\mathbf{w})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.75em;"></span><span class="strut bottom" style="height:1em;vertical-align:-0.25em;"></span><span class="base textstyle uncramped"><span class="mord mathit" style="margin-right:0.05764em;">E</span><span class="mopen">(</span><span class="mord textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span><span class="mclose">)</span></span></span></span> can be implemented by the SGD technique as described before, and <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mrow><mi mathvariant="bold">w</mi></mrow></mrow><annotation encoding="application/x-tex">\mathbf{w}</annotation><
 /semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.44444em;"></span><span class="strut bottom" style="height:0.44444em;vertical-align:0em;"></span><span class="base textstyle uncramped"><span class="mord textstyle uncramped"><span class="mord mathbf" style="margin-right:0.01597em;">w</span></span></span></span></span> itself is used as a &quot;model&quot; for future prediction.</p>
 <p>Interestingly, depending on a choice of loss and regularization function, prediction model you obtained will behave differently; even if one combination could work as a classifier, another choice might be appropriate for regression.</p>
-<p>Below we list possible options for <code>train_regression</code> and <code>train_classifier</code>, and this is the reason why these two functions are the most flexible in Hivemall:</p>
+<p>Below we list possible options for <code>train_regressor</code> and <code>train_classifier</code>, and this is the reason why these two functions are the most flexible in Hivemall:</p>
 <ul>
 <li><p>Loss function: <code>-loss</code>, <code>-loss_function</code></p>
 <ul>
-<li>For <code>train_regression</code><ul>
+<li>For <code>train_regressor</code><ul>
 <li>SquaredLoss (synonym: squared)</li>
 <li>QuantileLoss (synonym: quantile)</li>
-<li>EpsilonInsensitiveLoss (synonym: epsilon_intensitive)</li>
-<li>SquaredEpsilonInsensitiveLoss (synonym: squared_epsilon_intensitive)</li>
+<li>EpsilonInsensitiveLoss (synonym: epsilon_insensitive)</li>
+<li>SquaredEpsilonInsensitiveLoss (synonym: squared_epsilon_insensitive)</li>
 <li>HuberLoss (synonym: huber)</li>
 </ul>
 </li>
@@ -2297,8 +2327,8 @@ E(\mathbf{w}) := \frac{1}{n} \sum_{i=1}^{n} L(\mathbf{w}; \mathbf{x}_i, y_i) + \
 <li>The following losses are mainly designed for regression but can sometimes be useful in classification as well:<ul>
 <li>SquaredLoss (synonym: squared)</li>
 <li>QuantileLoss (synonym: quantile)</li>
-<li>EpsilonInsensitiveLoss (synonym: epsilon_intensitive)</li>
-<li>SquaredEpsilonInsensitiveLoss (synonym: squared_epsilon_intensitive)</li>
+<li>EpsilonInsensitiveLoss (synonym: epsilon_insensitive)</li>
+<li>SquaredEpsilonInsensitiveLoss (synonym: squared_epsilon_insensitive)</li>
 <li>HuberLoss (synonym: huber)</li>
 </ul>
 </li>
@@ -2315,9 +2345,9 @@ E(\mathbf{w}) := \frac{1}{n} \sum_{i=1}^{n} L(\mathbf{w}; \mathbf{x}_i, y_i) + \
 </ul>
 </li>
 </ul>
-<p>Additionally, there are several variants of the SGD technique, and it is also configureable as:</p>
+<p>Additionally, there are several variants of the SGD technique, and it is also configurable as:</p>
 <ul>
-<li>Optimizer <code>-opt</code>, <code>-optimizer</code><ul>
+<li>Optimizer: <code>-opt</code>, <code>-optimizer</code><ul>
 <li>SGD</li>
 <li>AdaGrad</li>
 <li>AdaDelta</li>
@@ -2325,9 +2355,30 @@ E(\mathbf{w}) := \frac{1}{n} \sum_{i=1}^{n} L(\mathbf{w}; \mathbf{x}_i, y_i) + \
 </ul>
 </li>
 </ul>
-<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>Option values are case insensitive and you can use <code>sgd</code> or <code>rda</code>, or <code>huberloss</code>.</p></div></div>
-<p>In practice, you can try different combinations of the options in order to achieve higher prediction accuracy.
-<div id="page-footer" class="localized-footer"><hr><!--
+<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>Option values are case insensitive and you can use <code>sgd</code> or <code>rda</code>, or <code>huberloss</code> in lower-case letters.</p></div></div>
+<p>Furthermore, optimizer offers to set auxiliary options such as:</p>
+<ul>
+<li>Number of iterations: <code>-iter</code>, <code>-iterations</code> [default: 10]<ul>
+<li>Repeat optimizer&apos;s learning procedure more than once to diligently find better result.</li>
+</ul>
+</li>
+<li>Convergence rate: <code>-cv_rate</code>, <code>-convergence_rate</code> [default: 0.005]<ul>
+<li>Define a stopping criterion for the iterative training.</li>
+<li>If the criterion is too small or too large, you may encounter over-fitting or under-fitting depending on value of <code>-iter</code> option.</li>
+</ul>
+</li>
+<li>Mini-batch size: <code>-mini_batch</code>, <code>-mini_batch_size</code> [default: 1]<ul>
+<li>Instead of learning samples one-by-one, this option enables optimizer to utilize multiple samples at once to minimize the error function.</li>
+<li>Appropriate mini-batch size leads efficient training and effective prediction model.</li>
+</ul>
+</li>
+</ul>
+<p>For details of available options, following queries might be helpful to list all of them:</p>
+<pre><code class="lang-sql"><span class="hljs-keyword">select</span> train_regressor(<span class="hljs-built_in">array</span>(), <span class="hljs-number">0</span>, <span class="hljs-string">&apos;-help&apos;</span>);
+<span class="hljs-keyword">select</span> train_classifier(<span class="hljs-built_in">array</span>(), <span class="hljs-number">0</span>, <span class="hljs-string">&apos;-help&apos;</span>);
+</code></pre>
+<p>In practice, you can try different combinations of the options in order to achieve higher prediction accuracy.</p>
+<p><div id="page-footer" class="localized-footer"><hr><!--
   Licensed to the Apache Software Foundation (ASF) under one
   or more contributor license agreements.  See the NOTICE file
   distributed with this work for additional information
@@ -2382,7 +2433,7 @@ 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/a98b42f8/userguide/misc/tokenizer.html
----------------------------------------------------------------------
diff --git a/userguide/misc/tokenizer.html b/userguide/misc/tokenizer.html
index b890479..db5cae3 100644
--- a/userguide/misc/tokenizer.html
+++ b/userguide/misc/tokenizer.html
@@ -244,7 +244,7 @@
                     
                         <b>1.3.1.</b>
                     
-                    Explicit addBias() for better prediction
+                    Explicit add_bias() for better prediction
             
                 </a>
             
@@ -707,14 +707,14 @@
         
         
     
-        <li class="chapter " data-level="4.1" data-path="../eval/stat_eval.html">
+        <li class="chapter " data-level="4.1" data-path="../eval/binary_classification_measures.html">
             
-                <a href="../eval/stat_eval.html">
+                <a href="../eval/binary_classification_measures.html">
             
                     
                         <b>4.1.</b>
                     
-                    Statistical evaluation of a prediction model
+                    Binary Classification Metrics
             
                 </a>
             
@@ -743,13 +743,43 @@
             
         </li>
     
-        <li class="chapter " data-level="4.2" data-path="../eval/rank.html">
+        <li class="chapter " data-level="4.2" data-path="../eval/multilabel_classification_measures.html">
             
-                <a href="../eval/rank.html">
+                <a href="../eval/multilabel_classification_measures.html">
             
                     
                         <b>4.2.</b>
                     
+                    Multi-label Classification Metrics
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="4.3" data-path="../eval/regression.html">
+            
+                <a href="../eval/regression.html">
+            
+                    
+                        <b>4.3.</b>
+                    
+                    Regression metrics
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="4.4" data-path="../eval/rank.html">
+            
+                <a href="../eval/rank.html">
+            
+                    
+                        <b>4.4.</b>
+                    
                     Ranking Measures
             
                 </a>
@@ -758,12 +788,12 @@
             
         </li>
     
-        <li class="chapter " data-level="4.3" data-path="../eval/datagen.html">
+        <li class="chapter " data-level="4.5" data-path="../eval/datagen.html">
             
                 <a href="../eval/datagen.html">
             
                     
-                        <b>4.3.</b>
+                        <b>4.5.</b>
                     
                     Data Generation
             
@@ -774,12 +804,12 @@
             <ul class="articles">
                 
     
-        <li class="chapter " data-level="4.3.1" data-path="../eval/lr_datagen.html">
+        <li class="chapter " data-level="4.5.1" data-path="../eval/lr_datagen.html">
             
                 <a href="../eval/lr_datagen.html">
             
                     
-                        <b>4.3.1.</b>
+                        <b>4.5.1.</b>
                     
                     Logistic Regression data generation
             
@@ -2214,8 +2244,8 @@
 <blockquote>
 <p>[smartcn, &#x4E3A;, apach, 2, 0, &#x534F;&#x8BAE;, &#x7684;, &#x5F00;&#x6E90;, &#x4E2D;&#x6587;, &#x5206;&#x8BCD;, &#x7CFB;&#x7EDF;, java, &#x8BED;&#x8A00;, &#x7F16;&#x5199;, &#x4FEE;&#x6539;, &#x7684;, &#x4E2D;&#x79D1;&#x9662;, &#x8BA1;&#x7B97;, &#x6240;, ictcla, &#x5206;&#x8BCD;, &#x7CFB;&#x7EDF;]</p>
 </blockquote>
-<p>For detailed APIs, please refer Javadoc of <a href="http://lucene.apache.org/core/5_3_1/analyzers-smartcn/org/apache/lucene/analysis/cn/smart/SmartChineseAnalyzer.html" target="_blank">SmartChineseAnalyzer</a> as well.
-<div id="page-footer" class="localized-footer"><hr><!--
+<p>For detailed APIs, please refer Javadoc of <a href="http://lucene.apache.org/core/5_3_1/analyzers-smartcn/org/apache/lucene/analysis/cn/smart/SmartChineseAnalyzer.html" target="_blank">SmartChineseAnalyzer</a> as well.</p>
+<p><div id="page-footer" class="localized-footer"><hr><!--
   Licensed to the Apache Software Foundation (ASF) under one
   or more contributor license agreements.  See the NOTICE file
   distributed with this work for additional information
@@ -2270,7 +2300,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
     <script>
         var gitbook = gitbook || [];
         gitbook.push(function() {
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/a98b42f8/userguide/misc/topk.html
----------------------------------------------------------------------
diff --git a/userguide/misc/topk.html b/userguide/misc/topk.html
index 4a986ab..2d26e1a 100644
--- a/userguide/misc/topk.html
+++ b/userguide/misc/topk.html
@@ -244,7 +244,7 @@
                     
                         <b>1.3.1.</b>
                     
-                    Explicit addBias() for better prediction
+                    Explicit add_bias() for better prediction
             
                 </a>
             
@@ -707,14 +707,14 @@
         
         
     
-        <li class="chapter " data-level="4.1" data-path="../eval/stat_eval.html">
+        <li class="chapter " data-level="4.1" data-path="../eval/binary_classification_measures.html">
             
-                <a href="../eval/stat_eval.html">
+                <a href="../eval/binary_classification_measures.html">
             
                     
                         <b>4.1.</b>
                     
-                    Statistical evaluation of a prediction model
+                    Binary Classification Metrics
             
                 </a>
             
@@ -743,13 +743,43 @@
             
         </li>
     
-        <li class="chapter " data-level="4.2" data-path="../eval/rank.html">
+        <li class="chapter " data-level="4.2" data-path="../eval/multilabel_classification_measures.html">
             
-                <a href="../eval/rank.html">
+                <a href="../eval/multilabel_classification_measures.html">
             
                     
                         <b>4.2.</b>
                     
+                    Multi-label Classification Metrics
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="4.3" data-path="../eval/regression.html">
+            
+                <a href="../eval/regression.html">
+            
+                    
+                        <b>4.3.</b>
+                    
+                    Regression metrics
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="4.4" data-path="../eval/rank.html">
+            
+                <a href="../eval/rank.html">
+            
+                    
+                        <b>4.4.</b>
+                    
                     Ranking Measures
             
                 </a>
@@ -758,12 +788,12 @@
             
         </li>
     
-        <li class="chapter " data-level="4.3" data-path="../eval/datagen.html">
+        <li class="chapter " data-level="4.5" data-path="../eval/datagen.html">
             
                 <a href="../eval/datagen.html">
             
                     
-                        <b>4.3.</b>
+                        <b>4.5.</b>
                     
                     Data Generation
             
@@ -774,12 +804,12 @@
             <ul class="articles">
                 
     
-        <li class="chapter " data-level="4.3.1" data-path="../eval/lr_datagen.html">
+        <li class="chapter " data-level="4.5.1" data-path="../eval/lr_datagen.html">
             
                 <a href="../eval/lr_datagen.html">
             
                     
-                        <b>4.3.1.</b>
+                        <b>4.5.1.</b>
                     
                     Logistic Regression data generation
             
@@ -2173,6 +2203,7 @@
 <li><a href="#tail-k">tail-K</a></li>
 </ul>
 </li>
+<li><a href="#alternative-approaches">Alternative approaches</a></li>
 </ul>
 
 </div><!-- tocstop -->
@@ -2891,7 +2922,57 @@ s05 <span class="hljs-keyword">as</span> (
 </tr>
 </tbody>
 </table>
-<p><div id="page-footer" class="localized-footer"><hr><!--
+<h1 id="alternative-approaches">Alternative approaches</h1>
+<p>In order to utilize mapper-side aggregation and reduce computational cost of shuffling, you can use <a href="generic_funcs.html#map-udafs"><code>to_ordered_map</code></a> or <a href="generic_funcs.html#list-udaf"><code>to_ordered_list</code></a> to get top/tail-k elements instead of <code>each_top_k</code>.</p>
+<p>As long as <code>key</code> is unique in each <code>id</code>, the following queries return same result:</p>
+<pre><code class="lang-sql">with t as (
+  <span class="hljs-keyword">select</span>
+    each_top_k(
+      <span class="hljs-number">10</span>, <span class="hljs-keyword">id</span>, <span class="hljs-keyword">key</span>,
+      <span class="hljs-keyword">id</span>, <span class="hljs-keyword">value</span>
+    ) <span class="hljs-keyword">as</span> (<span class="hljs-keyword">rank</span>, <span class="hljs-keyword">key</span>, <span class="hljs-keyword">id</span>, <span class="hljs-keyword">value</span>)
+  <span class="hljs-keyword">from</span> (
+    <span class="hljs-keyword">select</span>
+      *
+    <span class="hljs-keyword">from</span> 
+      <span class="hljs-keyword">test</span>
+    cluster <span class="hljs-keyword">by</span> 
+      <span class="hljs-keyword">id</span>
+  ) t
+)
+<span class="hljs-keyword">select</span> 
+  <span class="hljs-keyword">id</span>, collect_list(<span class="hljs-keyword">value</span>) <span class="hljs-keyword">as</span> topk
+<span class="hljs-keyword">from</span> 
+  t
+<span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span>
+  <span class="hljs-keyword">id</span>
+</code></pre>
+<pre><code class="lang-sql">with t as (
+  <span class="hljs-keyword">select</span>
+    <span class="hljs-keyword">id</span>, to_ordered_map(<span class="hljs-keyword">key</span>, <span class="hljs-keyword">value</span>, <span class="hljs-number">10</span>) <span class="hljs-keyword">as</span> m
+  <span class="hljs-keyword">from</span> 
+    <span class="hljs-keyword">test</span>
+  <span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span>
+    <span class="hljs-keyword">id</span>
+)
+<span class="hljs-keyword">select</span> 
+  <span class="hljs-keyword">id</span>, collect_list(<span class="hljs-keyword">value</span>) <span class="hljs-keyword">as</span> topk
+<span class="hljs-keyword">from</span> 
+  t
+lateral <span class="hljs-keyword">view</span> explode(m) t <span class="hljs-keyword">as</span> <span class="hljs-keyword">key</span>, <span class="hljs-keyword">value</span>
+<span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span>
+  <span class="hljs-keyword">id</span>
+</code></pre>
+<pre><code class="lang-sql"><span class="hljs-keyword">select</span> 
+  <span class="hljs-keyword">id</span>, to_ordered_list(<span class="hljs-keyword">value</span>, <span class="hljs-keyword">key</span>, <span class="hljs-string">&apos;-k 10&apos;</span>) <span class="hljs-keyword">as</span> topk
+<span class="hljs-keyword">from</span> 
+  <span class="hljs-keyword">test</span>
+<span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span>
+  <span class="hljs-keyword">id</span>
+</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>In case that <code>key</code> could duplicate in <code>id</code>, <code>to_ordered_map</code> behaves differently because key <code>K</code> is always unique in <code>Map&lt;K, V&gt;</code>.</p></div></div>
+<p>Similarly to <code>each_top_k</code>, tail-k can also be represented as: <code>to_ordered_map(key, value, -10)</code> and <code>to_ordered_list(value, key, &apos;-k -10&apos;)</code>.
+<div id="page-footer" class="localized-footer"><hr><!--
   Licensed to the Apache Software Foundation (ASF) under one
   or more contributor license agreements.  See the NOTICE file
   distributed with this work for additional information
@@ -2946,7 +3027,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
     <script>
         var gitbook = gitbook || [];
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/a98b42f8/userguide/multiclass/iris.html
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diff --git a/userguide/multiclass/iris.html b/userguide/multiclass/iris.html
index a2cab9b..2ce5605 100644
--- a/userguide/multiclass/iris.html
+++ b/userguide/multiclass/iris.html
@@ -244,7 +244,7 @@
                     
                         <b>1.3.1.</b>
                     
-                    Explicit addBias() for better prediction
+                    Explicit add_bias() for better prediction
             
                 </a>
             
@@ -707,14 +707,14 @@
         
         
     
-        <li class="chapter " data-level="4.1" data-path="../eval/stat_eval.html">
+        <li class="chapter " data-level="4.1" data-path="../eval/binary_classification_measures.html">
             
-                <a href="../eval/stat_eval.html">
+                <a href="../eval/binary_classification_measures.html">
             
                     
                         <b>4.1.</b>
                     
-                    Statistical evaluation of a prediction model
+                    Binary Classification Metrics
             
                 </a>
             
@@ -743,13 +743,43 @@
             
         </li>
     
-        <li class="chapter " data-level="4.2" data-path="../eval/rank.html">
+        <li class="chapter " data-level="4.2" data-path="../eval/multilabel_classification_measures.html">
             
-                <a href="../eval/rank.html">
+                <a href="../eval/multilabel_classification_measures.html">
             
                     
                         <b>4.2.</b>
                     
+                    Multi-label Classification Metrics
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="4.3" data-path="../eval/regression.html">
+            
+                <a href="../eval/regression.html">
+            
+                    
+                        <b>4.3.</b>
+                    
+                    Regression metrics
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="4.4" data-path="../eval/rank.html">
+            
+                <a href="../eval/rank.html">
+            
+                    
+                        <b>4.4.</b>
+                    
                     Ranking Measures
             
                 </a>
@@ -758,12 +788,12 @@
             
         </li>
     
-        <li class="chapter " data-level="4.3" data-path="../eval/datagen.html">
+        <li class="chapter " data-level="4.5" data-path="../eval/datagen.html">
             
                 <a href="../eval/datagen.html">
             
                     
-                        <b>4.3.</b>
+                        <b>4.5.</b>
                     
                     Data Generation
             
@@ -774,12 +804,12 @@
             <ul class="articles">
                 
     
-        <li class="chapter " data-level="4.3.1" data-path="../eval/lr_datagen.html">
+        <li class="chapter " data-level="4.5.1" data-path="../eval/lr_datagen.html">
             
                 <a href="../eval/lr_datagen.html">
             
                     
-                        <b>4.3.1.</b>
+                        <b>4.5.1.</b>
                     
                     Logistic Regression data generation
             
@@ -2210,7 +2240,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
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