[11/12] predictionio-site git commit: Documentation based on apache/predictionio#37c17935475cf9f753f6be4d9ab22dbbd7d3cd4a

[gi...@apache.org]

http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/8ce20376/evaluation/paramtuning/index.html
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diff --git a/evaluation/paramtuning/index.html b/evaluation/paramtuning/index.html
index acc21b1..229b890 100644
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@@ -11,11 +11,9 @@
 <span class="o">}</span>
 </pre></td></tr></tbody></table> </div> <h3 id='build-and-run-the-evaluation' class='header-anchors'>Build and run the evaluation</h3><p>To run an evaluation, the command <code>pio eval</code> is used. It takes two mandatory parameter, 1. the <code>Evaluation</code> object, which tells PredictionIO the engine and metric we use for the evaluation; and 2. the <code>EngineParamsGenerator</code>, which contains a list of engine params to test against. The following command kickstarts the evaluation workflow for the classification template.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
-3
-4</pre></td><td class="code"><pre><span class="gp">$ </span>pio build
+3</pre></td><td class="code"><pre><span class="gp">$ </span>pio build
 ...
-<span class="gp">$ </span>pio <span class="nb">eval </span>org.template.classification.AccuracyEvaluation <span class="se">\</span>
-    org.template.classification.EngineParamsList
+<span class="gp">$ </span>pio <span class="nb">eval </span>org.example.classification.AccuracyEvaluation org.example.classification.EngineParamsList
 </pre></td></tr></tbody></table> </div> <p>You will see the following output:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
@@ -102,7 +100,7 @@ Optimal Engine Params:
   <span class="o">}</span>
 <span class="o">}</span>
 Metrics:
-  org.template.classification.Accuracy: 0.9281045751633987
+  org.example.classification.Accuracy: 0.9281045751633987
 The best variant params can be found <span class="k">in </span>best.json
 <span class="o">[</span>INFO] <span class="o">[</span>CoreWorkflow<span class="nv">$]</span> runEvaluation completed
 </pre></td></tr></tbody></table> </div> <p>The console prints out the evaluation metric score of each engine params, and finally pretty print the optimal engine params. Amongst the 3 engine params we evaluate, <em>lambda = 10.0</em> yields the highest accuracy score of ~0.9281.</p><h3 id='deploy-the-best-engine-parameter' class='header-anchors'>Deploy the best engine parameter</h3><p>The evaluation module also writes out the best engine parameter to disk at <code>best.json</code>. We can train and deploy this specify engine variant using the extra parameter <code>-v</code>. For example:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
@@ -291,11 +289,9 @@ The best variant params can be found <span class="k">in </span>best.json
 <span class="o">}</span>
 </pre></td></tr></tbody></table> </div> <p>A good practise is to first define a base engine params, it contains the common parameters used in all evaluations (lines 7 to 8). With the base params, we construct the list of engine params we want to evaluation by adding or replacing the controller parameter. Lines 13 to 16 generate 3 engine parameters, each has a different smoothing parameters.</p><h2 id='running-the-evaluation' class='header-anchors'>Running the Evaluation</h2><p>It remains to run the evaluation. Let&#39;s recap the quick start section above. The <code>pio eval</code> command kick starts the evaluation, and the result can be seen from the console.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
-3
-4</pre></td><td class="code"><pre><span class="gp">$ </span>pio build
+3</pre></td><td class="code"><pre><span class="gp">$ </span>pio build
 ...
-<span class="gp">$ </span>pio <span class="nb">eval </span>org.template.classification.AccuracyEvaluation <span class="se">\</span>
-    org.template.classification.EngineParamsList
+<span class="gp">$ </span>pio <span class="nb">eval </span>org.example.classification.AccuracyEvaluation org.example.classification.EngineParamsList
 </pre></td></tr></tbody></table> </div> <p>You will see the following output:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
 2
 3
@@ -390,4 +386,4 @@ The best variant params can be found <span class="k">in </span>best.json
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