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hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">ML Tuning and Evaluation</a><span 
class="spacer">&gt;</span></li><li><span 
class="last">Overview</span></li></ul></div><div id="page-title"><h1>ML Tuning 
and Evaluation</h1></div></div><div id="table-of-content-wrapper"><a 
id="edit-page
 -link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/evaluation/index.html.md";><img
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class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">ML Tuning and 
Evaluation</a><span class="spacer">&gt;</span></li><li><span 
class="last">Overview</span></li></ul></div><div id="page-title"><h1>ML Tuning 
and Evaluation</h1></div></div><div class="content"> <p>PredictionIO&#39;s 
evaluation module allows you to streamline the process of testing lots of knobs 
in engine parameters and deploy the best one out of it using statisically sound 
cross-validation methods.</p><p>There are two key components:</p><h3 
id='engine' class='header-anchors'>Engine</h3><p>It is our evaluation target. 
During evaluation, in addition to the <em>train</em> and <em>deploy</em> mode 
we describe in earlier sections, the engine also generates a list of t
 esting data points. These data points are a sequence of <em>Query</em> and 
<em>Actual Result</em> tuples. <em>Queries</em> are sent to the engine and the 
engine responds with a <em>Predicted Result</em>, in the same way as how the 
engine serves a query.</p><h3 id='evaluator' 
class='header-anchors'>Evaluator</h3><p>The evaluator joins the sequence of 
<em>Query</em>, <em>Predicted Result</em>, and <em>Actual Result</em> together 
and evaluates the quality of the engine. PredictionIO enables you to implement 
any metric with just a few lines of code.</p><p><img alt="PredictionIO 
Evaluation Overview" src="/images/engine-evaluation-4ab1bfeb.png"/></p><p>We 
will discuss various aspects of evaluation with PredictionIO.</p> <ul> <li><a 
href="/evaluation/paramtuning/">Hyperparameter Tuning</a> - it is an end-to-end 
example of using PredictionIO evaluation module to select and deploy the best 
engine parameter.</li> <li><a 
href="/evaluation/evaluationdashboard/">Evaluation Dashboard</a> - it is 
 the dashboard where you can see a detailed breakdown of all previous 
evaluations.</li> <li><a href="/evaluation/metricchoose/">Choosing Evaluation 
Metrics</a> - we cover some basic machine learning metrics</li> <li><a 
href="/evaluation/metricbuild/">Bulding Evaluation Metrics</a> - we illustrate 
how to implement a custom metric with as few as one line of code (plus some 
boilerplates).</li> </ul> </div></div></div></div><footer><div 
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Evaluation</a><span class="spacer">&gt;</span></li><li><span 
class="last">Overview</span></li></ul></div><div id="page-title"><h1>ML Tuning 
and Evaluation</h1></div></div><div class="content"> <p>PredictionIO&#39;s 
evaluation module allows you to streamline the process of testing lots of knobs 
in engine parameters and deploy the best one out of it using statisically sound 
cross-validation methods.</p><p>There are two key components:</p><h3 
id='engine' class='header-anchors'>Engine</h3><p>It is our evaluation target. 
During evaluation, in addition to the <em>train</em> and <em>deploy</em> mode 
we describe in earlier sections, the engine also generates a list of testing 
data points. 
 These data points are a sequence of <em>Query</em> and <em>Actual Result</em> 
tuples. <em>Queries</em> are sent to the engine and the engine responds with a 
<em>Predicted Result</em>, in the same way as how the engine serves a 
query.</p><h3 id='evaluator' class='header-anchors'>Evaluator</h3><p>The 
evaluator joins the sequence of <em>Query</em>, <em>Predicted Result</em>, and 
<em>Actual Result</em> together and evaluates the quality of the engine. 
PredictionIO enables you to implement any metric with just a few lines of 
code.</p><p><img alt="PredictionIO Evaluation Overview" 
src="/images/engine-evaluation-4ab1bfeb.png"/></p><p>We will discuss various 
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Serving</span></a></li><li class="level-3"><a class="final" 
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href="/templates/recommendation/blacklist-items/"><span>Filter Recommended 
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href="/templates/recommendatio
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hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">ML Tuning and Evaluation</a><span 
class="spacer">&gt;</span></li><li><span class="last">Building Evaluation 
Metrics</span></li></ul></div><div id="page-title"><h1>Building Evaluation 
Metrics</h1></di
 v></div><div id="table-of-content-wrapper"><h5>On this page</h5><aside 
id="table-of-contents"><ul> <li> <a href="#overview">Overview</a> </li> <li> <a 
href="#example-1-accuracy-metric">Example 1: Accuracy Metric</a> </li> <li> <a 
href="#example-2-precision-metric">Example 2: Precision Metric</a> </li> </ul> 
</aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/evaluation/metricbuild.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
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class="hidden-sm hidden xs"><ul><li><a href="#">ML Tuning and 
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class="last">Building Evaluation Metrics</span></li></ul></div><div 
id="page-title"><h1>Building Evaluation Metrics</h1></div></div><div 
class="content"> <p>PredictionIO enables developer to implement evaluation 
custom evaluation metric with just a few lines of cod
 e. We illustrate it with <a href="/templates/classification/quickstart/">the 
classification template</a>.</p><h2 id='overview' 
class='header-anchors'>Overview</h2><p>A simplistic form of metric is a 
function which takes a <code>(Query, PredictedResult, 
ActualResult)</code>-tuple (<em>QPA-tuple</em>) as input and return a score. 
Exploiting this properties allows us to implement custom metric with a single 
line of code (plus some boilerplates). We demonstate this with two metrics: 
accuracy and precision.</p> <h2 id='example-1:-accuracy-metric' 
class='header-anchors'>Example 1: Accuracy Metric</h2><p>Accuracy is a metric 
capturing the portion of correct prediction among all test data points. A way 
to model this is for each correct QPA-tuple, we give a score of 1.0 and 
otherwise 0.0, then we take an average of all tuple scores.</p><p>PredictionIO 
has a [[AverageMetric]] helper class which provides this feature. This class 
takes 4 type parameters, [[EvalInfo]], [[Query]], [[PredictedResu
 lt]], and [[ActualResult]], these types can be found from the engine&#39;s 
signature. Line 5 below is the custom calculation.</p><div class="highlight 
scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1
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class="final" href="/install/"><span>Installing Apache 
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href="/start/download/"><span>Downloading an Engine Template</span></a></li><li 
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 nalyzing Data</span></a><ul><li class="level-2"><a class="final" 
href="/datacollection/"><span>Event Server Overview</span></a></li><li 
class="level-2"><a class="final" 
href="/datacollection/eventapi/"><span>Collecting Data with 
REST/SDKs</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/eventmodel/"><span>Events Modeling</span></a></li><li 
class="level-2"><a class="final" 
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href="/datacollection/channel/"><span>Channel</span></a></li><li 
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href="/datacollection/analytics/"><span>Using Analytics 
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href="/datacollection/plugin/"><span>Event Server 
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class="final" href="/algorithm/"><span>Built-in Algorithm 
Libraries</span></a></li><li class="level-2"><a class="final" 
href="/algorithm/switch/"><span>Switching to Another 
Algorithm</span></a></li><li class="level-2"><a class="final" 
href="/algorithm/multiple/"><span>Combining Multiple 
Algorithms</span></a></li><li class="level-2"><a class="final" 
href="/algorithm/custom/"><span>Adding Your Own 
Algorithms</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>ML Tuning and Evaluation</span></a><ul><li class="level-2"><a 
class="final" href="/evaluation/"><span>Overview</span></a></li><li 
class="level-2"><a class="final" 
href="/evaluation/paramtuning/"><span>Hyperparameter Tuning</span></a></li><li 
class="level-2"><a class="final" 
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hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">ML Tuning and Evaluation</a><span 
class="spacer">&gt;</span></li><li><span class="last">Building Evaluation 
Metrics</span></li></ul></div><div id="page-title"><h1>Building Evaluation 
Metrics</h1></div></div><d
 iv id="table-of-content-wrapper"><h5>On this page</h5><aside 
id="table-of-contents"><ul> <li> <a href="#overview">Overview</a> </li> <li> <a 
href="#example-1-accuracy-metric">Example 1: Accuracy Metric</a> </li> <li> <a 
href="#example-2-precision-metric">Example 2: Precision Metric</a> </li> </ul> 
</aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/evaluation/metricbuild.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
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class="hidden-sm hidden xs"><ul><li><a href="#">ML Tuning and 
Evaluation</a><span class="spacer">&gt;</span></li><li><span 
class="last">Building Evaluation Metrics</span></li></ul></div><div 
id="page-title"><h1>Building Evaluation Metrics</h1></div></div><div 
class="content"> <p>PredictionIO enables developer to implement evaluation 
custom evaluation metric with just a few lines of code. We illustrate it 
 with <a href="/templates/classification/quickstart/">the classification 
template</a>.</p><h2 id='overview' class='header-anchors'>Overview</h2><p>A 
simplistic form of metric is a function which takes a <code>(Query, 
PredictedResult, ActualResult)</code>-tuple (<em>QPA-tuple</em>) as input and 
return a score. Exploiting this properties allows us to implement custom metric 
with a single line of code (plus some boilerplates). We demonstate this with 
two metrics: accuracy and precision.</p> <h2 id='example-1:-accuracy-metric' 
class='header-anchors'>Example 1: Accuracy Metric</h2><p>Accuracy is a metric 
capturing the portion of correct prediction among all test data points. A way 
to model this is for each correct QPA-tuple, we give a score of 1.0 and 
otherwise 0.0, then we take an average of all tuple scores.</p><p>PredictionIO 
has a [[AverageMetric]] helper class which provides this feature. This class 
takes 4 type parameters, [[EvalInfo]], [[Query]], [[PredictedResult]], and 
[[ActualRe
 sult]], these types can be found from the engine&#39;s signature. Line 5 below 
is the custom calculation.</p><div class="highlight scala"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1
 2
 3
 4
@@ -143,7 +143,7 @@ Optimal Engine Params:
 <span class="o">}</span>
 Metrics:
   org.template.classification.Precision: 0.8846153846153846
-</pre></td></tr></tbody></table> </div> <p>(See 
MyClassification/src/main/scala/<strong><em>PrecisionEvaluation.scala</em></strong>
 for the full usage.)</p></div></div></div></div><footer><div 
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