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 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
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><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/predictionio/tree/livedoc/docs/manual/source/evaluation/metricbuild.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
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">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 wit
 h <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 demonstrate 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 
[[ActualResu
 lt]], 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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Started</span></a><ul><li class="level-2"><a class="final" 
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class="final" href="/install/"><span>Installing Apache 
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class="level-2"><a class="final" href="/start/deploy/"><span>Deploying Your 
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class="final" href="/sdk/ruby/"><span>Ruby SDK</span></a></li><li 
class="level-3"><a class="final" href="/sdk/community/"><span>Community Powered 
SDKs</span></a></li></ul></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>Deploying an Engine</span></a><ul><li 
class="level-2"><a class="final" href="/deploy/"><span>Deploying as a Web 
Service</span></a></li><li class="level-2"><a class="final" 
href="/batchpredict/"><span>Batch Predictions</span></a></li><li 
class="level-2"><a class="final" href="/deploy/m
 onitoring/"><span>Monitoring Engine</span></a></li><li class="level-2"><a 
class="final" href="/deploy/engineparams/"><span>Setting Engine 
Parameters</span></a></li><li class="level-2"><a class="final" 
href="/deploy/enginevariants/"><span>Deploying Multiple Engine 
Variants</span></a></li><li class="level-2"><a class="final" 
href="/deploy/plugin/"><span>Engine Server Plugin</span></a></li></ul></li><li 
class="level-1"><a class="expandible" href="#"><span>Customizing an 
Engine</span></a><ul><li class="level-2"><a class="final" 
href="/customize/"><span>Learning DASE</span></a></li><li class="level-2"><a 
class="final" href="/customize/dase/"><span>Implement DASE</span></a></li><li 
class="level-2"><a class="final" 
href="/customize/troubleshooting/"><span>Troubleshooting Engine 
Development</span></a></li><li class="level-2"><a class="final" 
href="/api/current/#package"><span>Engine Scala 
APIs</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Collecting and A
 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" 
href="/datacollection/webhooks/"><span>Unifying Multichannel Data with 
Webhooks</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/channel/"><span>Channel</span></a></li><li 
class="level-2"><a class="final" 
href="/datacollection/batchimport/"><span>Importing Data in 
Batch</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/analytics/"><span>Using Analytics 
Tools</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/plugin/"><span>Event Server 
Plugin</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><s
 pan>Choosing an Algorithm</span></a><ul><li class="level-2"><a 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>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" 
href="/evaluation/evaluationdashboard/"><span>Evaluation 
Dashboard</span></a></li><li class="level-2"><a class="final" 
href="/evaluation/metricchoose/"><span>Choosing Eva
 luation Metrics</span></a></li><li class="level-2"><a class="final active" 
href="/evaluation/metricbuild/"><span>Building Evaluation 
Metrics</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>System Architecture</span></a><ul><li class="level-2"><a 
class="final" href="/system/"><span>Architecture Overview</span></a></li><li 
class="level-2"><a class="final" href="/system/anotherdatastore/"><span>Using 
Another Data Store</span></a></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>PredictionIO® Official 
Templates</span></a><ul><li class="level-2"><a class="final" 
href="/templates/"><span>Intro</span></a></li><li class="level-2"><a 
class="expandible" href="#"><span>Recommendation</span></a><ul><li 
class="level-3"><a class="final" 
href="/templates/recommendation/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/dase/"><span>DASE</span></a></li><li 
class="level-3"><a clas
 s="final" href="/templates/recommendation/evaluation/"><span>Evaluation 
Explained</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/recommendation/reading-custom-events/"><span>Read Custom 
Events</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-data-prep/"><span>Customize Data 
Preparator</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-serving/"><span>Customize 
Serving</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/training-with-implicit-preference/"><span>Train 
with Implicit Preference</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/blacklist-items/"><span>Filter Recommended 
Items by Blacklist in Query</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/batch-evaluato
 r/"><span>Batch Persistable Evaluator</span></a></li></ul></li><li 
class="level-2"><a class="expandible" href="#"><span>E-Commerce 
Recommendation</span></a><ul><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/how-to/"><span>How-To</span></a></li><li
 class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/train-with-rate-event/"><span>Train 
with Rate Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/adjust-score/"><span>Adjust 
Score</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>Similar Product</span></a><ul><li class="level-3"><a 
class="final" href="/templates/similarproduct/quickstart/"><span>Quick 
Start</span></a></li><li
  class="level-3"><a class="final" 
href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple 
Events and Multiple Algorithms</span></a></li><li class="level-3"><a 
class="final" 
href="/templates/similarproduct/return-item-properties/"><span>Returns Item 
Properties</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate 
Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events 
for Users</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/recommended-user/"><span>Recommend 
Users</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>Classification</span></a><ul>
 <li class="level-3"><a class="final" 
href="/templates/classification/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/add-algorithm/"><span>Use Alternative 
Algorithm</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/reading-custom-properties/"><span>Read Custom 
Properties</span></a></li></ul></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>Engine Template Gallery</span></a><ul><li 
class="level-2"><a class="final" 
href="/gallery/template-gallery/"><span>Browse</span></a></li><li 
class="level-2"><a class="final" 
href="/community/submit-template/"><span>Submit your Engine as a 
Template</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#">
 <span>Demo Tutorials</span></a><ul><li class="level-2"><a class="final" 
href="/demo/tapster/"><span>Comics Recommendation Demo</span></a></li><li 
class="level-2"><a class="final" href="/demo/community/"><span>Community 
Contributed Demo</span></a></li><li class="level-2"><a class="final" 
href="/demo/textclassification/"><span>Text Classification Engine 
Tutorial</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="/community/"><span>Getting Involved</span></a><ul><li class="level-2"><a 
class="final" href="/community/contribute-code/"><span>Contribute 
Code</span></a></li><li class="level-2"><a class="final" 
href="/community/contribute-documentation/"><span>Contribute 
Documentation</span></a></li><li class="level-2"><a class="final" 
href="/community/contribute-sdk/"><span>Contribute a SDK</span></a></li><li 
class="level-2"><a class="final" 
href="/community/contribute-webhook/"><span>Contribute a 
Webhook</span></a></li><li class="level-2"><a class="final" href="/commu
 nity/projects/"><span>Community Projects</span></a></li></ul></li><li 
class="level-1"><a class="expandible" href="#"><span>Getting 
Help</span></a><ul><li class="level-2"><a class="final" 
href="/resources/faq/"><span>FAQs</span></a></li><li class="level-2"><a 
class="final" href="/support/"><span>Support</span></a></li></ul></li><li 
class="level-1"><a class="expandible" 
href="#"><span>Resources</span></a><ul><li class="level-2"><a class="final" 
href="/cli/"><span>Command-line Interface</span></a></li><li class="level-2"><a 
class="final" href="/resources/release/"><span>Release 
Cadence</span></a></li><li class="level-2"><a class="final" 
href="/resources/intellij/"><span>Developing Engines with IntelliJ 
IDEA</span></a></li><li class="level-2"><a class="final" 
href="/resources/upgrade/"><span>Upgrade Instructions</span></a></li><li 
class="level-2"><a class="final" 
href="/resources/glossary/"><span>Glossary</span></a></li></ul></li><li 
class="level-1"><a class="expandible" href="#"><span>
 Apache Software Foundation</span></a><ul><li class="level-2"><a class="final" 
href="https://www.apache.org/";><span>Apache Homepage</span></a></li><li 
class="level-2"><a class="final" 
href="https://www.apache.org/licenses/";><span>License</span></a></li><li 
class="level-2"><a class="final" 
href="https://www.apache.org/foundation/sponsorship.html";><span>Sponsorship</span></a></li><li
 class="level-2"><a class="final" 
href="https://www.apache.org/foundation/thanks.html";><span>Thanks</span></a></li><li
 class="level-2"><a class="final" 
href="https://www.apache.org/security/";><span>Security</span></a></li></ul></li></ul></nav></div><div
 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">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 id="ta
 ble-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 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">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 demonstrate 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 
[[ActualResult]], the
 se 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
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-<!DOCTYPE html><html><head><title>Choosing Evaluation Metrics</title><meta 
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href="/"><span>Welcome to Apache PredictionIO®</span></a></li></ul></li><li 
class="level-1"><a class="expandible" href="#"><span>Getting 
Started</span></a><ul><li class="level-2"><a class="final" 
href="/start/"><span>A Quick Intro</span></a></li><li class="level-2"><a 
class="final" href="/install/"><span>Installing Apache 
PredictionIO</span></a></li><li class="level-2"><a class="final" 
href="/start/download/"><span>Downloading an Engine Template</span></a></li><li 
class="level-2"><a class="final" href="/start/deploy/"><span>Deploying Your 
First Engine</span></a></li><li class="level-2"><a class="final" 
href="/start/customize/"><span>Customizing the 
Engine</span></a></li></ul></li><li class="level-1"><a class="expandible" 
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 l-2"><a class="final" href="/appintegration/"><span>App Integration 
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class="final" href="/sdk/java/"><span>Java & Android SDK</span></a></li><li 
class="level-3"><a class="final" href="/sdk/php/"><span>PHP 
SDK</span></a></li><li class="level-3"><a class="final" 
href="/sdk/python/"><span>Python SDK</span></a></li><li class="level-3"><a 
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class="level-3"><a class="final" href="/sdk/community/"><span>Community Powered 
SDKs</span></a></li></ul></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>Deploying an Engine</span></a><ul><li 
class="level-2"><a class="final" href="/deploy/"><span>Deploying as a Web 
Service</span></a></li><li class="level-2"><a class="final" 
href="/batchpredict/"><span>Batch Predictions</span></a></li><li 
class="level-2"><a class="final" href="/deploy/
 monitoring/"><span>Monitoring Engine</span></a></li><li class="level-2"><a 
class="final" href="/deploy/engineparams/"><span>Setting Engine 
Parameters</span></a></li><li class="level-2"><a class="final" 
href="/deploy/enginevariants/"><span>Deploying Multiple Engine 
Variants</span></a></li><li class="level-2"><a class="final" 
href="/deploy/plugin/"><span>Engine Server Plugin</span></a></li></ul></li><li 
class="level-1"><a class="expandible" href="#"><span>Customizing an 
Engine</span></a><ul><li class="level-2"><a class="final" 
href="/customize/"><span>Learning DASE</span></a></li><li class="level-2"><a 
class="final" href="/customize/dase/"><span>Implement DASE</span></a></li><li 
class="level-2"><a class="final" 
href="/customize/troubleshooting/"><span>Troubleshooting Engine 
Development</span></a></li><li class="level-2"><a class="final" 
href="/api/current/#package"><span>Engine Scala 
APIs</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Collecting and 
 Analyzing 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" 
href="/datacollection/webhooks/"><span>Unifying Multichannel Data with 
Webhooks</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/channel/"><span>Channel</span></a></li><li 
class="level-2"><a class="final" 
href="/datacollection/batchimport/"><span>Importing Data in 
Batch</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/analytics/"><span>Using Analytics 
Tools</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/plugin/"><span>Event Server 
Plugin</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><
 span>Choosing an Algorithm</span></a><ul><li class="level-2"><a 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" 
href="/evaluation/evaluationdashboard/"><span>Evaluation 
Dashboard</span></a></li><li class="level-2"><a class="final active" 
href="/evaluation/metricchoose/"><span>C
 hoosing Evaluation Metrics</span></a></li><li class="level-2"><a class="final" 
href="/evaluation/metricbuild/"><span>Building Evaluation 
Metrics</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>System Architecture</span></a><ul><li class="level-2"><a 
class="final" href="/system/"><span>Architecture Overview</span></a></li><li 
class="level-2"><a class="final" href="/system/anotherdatastore/"><span>Using 
Another Data Store</span></a></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>PredictionIO® Official 
Templates</span></a><ul><li class="level-2"><a class="final" 
href="/templates/"><span>Intro</span></a></li><li class="level-2"><a 
class="expandible" href="#"><span>Recommendation</span></a><ul><li 
class="level-3"><a class="final" 
href="/templates/recommendation/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/dase/"><span>DASE</span></a></li><li 
class="level-3"><a 
 class="final" href="/templates/recommendation/evaluation/"><span>Evaluation 
Explained</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/recommendation/reading-custom-events/"><span>Read Custom 
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href="#">ML Tuning and Evaluation</a><span 
class="spacer">&gt;</span></li><li><span class="last">Choosing Evaluation 
Metrics</span></li></ul></div><div id="page-title"><h1>Choosing Evaluation 
Metrics</h1></div></div><div
  id="table-of-content-wrapper"><h5>On this page</h5><aside 
id="table-of-contents"><ul> <li> <a href="#defining-metric">Defining Metric</a> 
</li> <li> <a href="#common-metrics">Common Metrics</a> </li> </ul> 
</aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/evaluation/metricchoose.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">Choosing Evaluation Metrics</span></li></ul></div><div 
id="page-title"><h1>Choosing Evaluation Metrics</h1></div></div><div 
class="content"> <p>The <a href="/evaluation/paramtuning/">hyperparameter 
tuning module</a> allows us to select the optimal engine parameter defined by a 
<code>Metric</code>. <code>Metric</code> determines the quality of an engine 
variant.
  We have skimmed through the process of choosing the right <code>Metric</code> 
in previous sections.</p><p>This section discusses basic evaluation metrics 
commonly used for classification problems. If you are more interested in 
knowing how to <em>implement</em> a custom metric, please skip to <a 
href="/evaluation/metricbuild/">the next section</a>.</p><h2 
id='defining-metric' class='header-anchors'>Defining Metric</h2><p>Metric 
evaluates the quality of an engine by comparing engine&#39;s output (predicted 
result) with the original label (actual result). A engine serving better 
prediction should yield a higher metric score, the tuning module returns the 
engine parameter with the highest score. It is sometimes called <a 
href="http://en.wikipedia.org/wiki/Loss_function";><em>loss function</em></a> in 
literature, where the goal is to minimize the loss function.</p><p>During 
tuning, it is important for us to understand the definition of the metric, to 
make sure it is aligned with the pred
 iction engine&#39;s goal.</p><p>In the classification template, we use 
<em>Accuracy</em> as our metric. <em>Accuracy</em> is defined as: the 
percentage of queries which the engine is able to predict the correct 
label.</p><h2 id='common-metrics' class='header-anchors'>Common 
Metrics</h2><p>We illustrate the choice of metric with the following confusion 
matrix. Row represents the engine predicted label, column represents the actual 
label. The second row means that of the 200 testing data points, the engine 
predicted 60 (15 + 35 + 10) of them as label 2.0, among which 35 are correct 
prediction (i.e. actual label is 2.0, matches with the prediction), and 25 are 
wrong.</p> <table><thead> <tr> <th style="text-align: center"></th> <th 
style="text-align: center">Actual = 1.0</th> <th style="text-align: 
center">Actual = 2.0</th> <th style="text-align: center">Actual = 3.0</th> 
</tr> </thead><tbody> <tr> <td style="text-align: center"><strong>Predicted = 
1.0</strong></td> <td style="text-alig
 n: center">30</td> <td style="text-align: center">0</td> <td 
style="text-align: center">60</td> </tr> <tr> <td style="text-align: 
center"><strong>Predicted = 2.0</strong></td> <td style="text-align: 
center">15</td> <td style="text-align: center">35</td> <td style="text-align: 
center">10</td> </tr> <tr> <td style="text-align: center"><strong>Predicted = 
3.0</strong></td> <td style="text-align: center">0</td> <td style="text-align: 
center">0</td> <td style="text-align: center">50</td> </tr> </tbody></table> 
<h3 id='accuracy' class='header-anchors'>Accuracy</h3><p>Accuracy means that 
how many data points are predicted correctly. It is one of the simplest form of 
evaluation metrics. The accuracy score is # of correct points / # total = (30 + 
35 + 50) / 200 = 0.575.</p><h3 id='precision' 
class='header-anchors'>Precision</h3><p>Precision is a metric for binary 
classifier which measures the correctness among all positive labels. A binary 
classifier gives only two output values (i.e. positi
 ve and negative). For problem where there are multiple values (3 in our 
example), we first have to transform our problem into a binary classification 
problem. For example, we can have problem whether label = 1.0. The confusion 
matrix now becomes:</p> <table><thead> <tr> <th style="text-align: 
center"></th> <th style="text-align: center">Actual = 1.0</th> <th 
style="text-align: center">Actual != 1.0</th> </tr> </thead><tbody> <tr> <td 
style="text-align: center"><strong>Predicted = 1.0</strong></td> <td 
style="text-align: center">30</td> <td style="text-align: center">60</td> </tr> 
<tr> <td style="text-align: center"><strong>Predicted != 1.0</strong></td> <td 
style="text-align: center">15</td> <td style="text-align: center">95</td> </tr> 
</tbody></table> <p>Precision is the ratio between the number of correct 
positive answer (true positive) and the sum of correct positive answer (true 
positive) and wrong but positively labeled answer (false positive). In this 
case, the precision is 30
  / (30 + 60) = ~0.3333.</p><h3 id='recall' 
class='header-anchors'>Recall</h3><p>Recall is a metric for binary classifier 
which measures how many positive labels are successfully predicted amongst all 
positive labels. Formally, it is the ratio between the number of correct 
positive answer (true positive) and the sum of correct positive answer (true 
positive) and wrongly negatively labeled answer (false negative). In this case, 
the recall is 30 / (30 + 15) = ~0.6667.</p><p>As we have discussed several 
common metrics for classification problem, we can implement them using the 
<code>Metric</code> class in <a href="/evaluation/metricbuild">the next 
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 >hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
 >href="#">Tuning and Evaluation</a><span 
 >class="spacer">&gt;</span></li><li><span class="last">Choosing Evaluation 
 >Metrics</span></li></ul></div><div id="page-title"><h1>Choosing Evaluation 
 >Metrics</h1></div></div><div id="t
 able-of-content-wrapper"><h5>On this page</h5><aside 
id="table-of-contents"><ul> <li> <a href="#defining-metric">Defining Metric</a> 
</li> <li> <a href="#common-metrics">Common Metrics</a> </li> </ul> 
</aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/evaluation/metricchoose.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">Tuning and Evaluation</a><span 
class="spacer">&gt;</span></li><li><span class="last">Choosing Evaluation 
Metrics</span></li></ul></div><div id="page-title"><h1>Choosing Evaluation 
Metrics</h1></div></div><div class="content"> <p>The <a 
href="/evaluation/paramtuning/">hyperparameter tuning module</a> allows us to 
select the optimal engine parameter defined by a <code>Metric</code>. 
<code>Metric</code> determines the quality of an engine variant. We have 
 skimmed through the process of choosing the right <code>Metric</code> in 
previous sections.</p><p>This section discusses basic evaluation metrics 
commonly used for classification problems. If you are more interested in 
knowing how to <em>implement</em> a custom metric, please skip to <a 
href="/evaluation/metricbuild/">the next section</a>.</p><h2 
id='defining-metric' class='header-anchors'>Defining Metric</h2><p>Metric 
evaluates the quality of an engine by comparing engine&#39;s output (predicted 
result) with the original label (actual result). A engine serving better 
prediction should yield a higher metric score, the tuning module returns the 
engine parameter with the highest score. It is sometimes called <a 
href="http://en.wikipedia.org/wiki/Loss_function";><em>loss function</em></a> in 
literature, where the goal is to minimize the loss function.</p><p>During 
tuning, it is important for us to understand the definition of the metric, to 
make sure it is aligned with the prediction en
 gine&#39;s goal.</p><p>In the classification template, we use 
<em>Accuracy</em> as our metric. <em>Accuracy</em> is defined as: the 
percentage of queries which the engine is able to predict the correct 
label.</p><h2 id='common-metrics' class='header-anchors'>Common 
Metrics</h2><p>We illustrate the choice of metric with the following confusion 
matrix. Row represents the engine predicted label, column represents the actual 
label. The second row means that of the 200 testing data points, the engine 
predicted 60 (15 + 35 + 10) of them as label 2.0, among which 35 are correct 
prediction (i.e. actual label is 2.0, matches with the prediction), and 25 are 
wrong.</p> <table><thead> <tr> <th style="text-align: center"></th> <th 
style="text-align: center">Actual = 1.0</th> <th style="text-align: 
center">Actual = 2.0</th> <th style="text-align: center">Actual = 3.0</th> 
</tr> </thead><tbody> <tr> <td style="text-align: center"><strong>Predicted = 
1.0</strong></td> <td style="text-align: center
 ">30</td> <td style="text-align: center">0</td> <td style="text-align: 
center">60</td> </tr> <tr> <td style="text-align: center"><strong>Predicted = 
2.0</strong></td> <td style="text-align: center">15</td> <td style="text-align: 
center">35</td> <td style="text-align: center">10</td> </tr> <tr> <td 
style="text-align: center"><strong>Predicted = 3.0</strong></td> <td 
style="text-align: center">0</td> <td style="text-align: center">0</td> <td 
style="text-align: center">50</td> </tr> </tbody></table> <h3 id='accuracy' 
class='header-anchors'>Accuracy</h3><p>Accuracy means that how many data points 
are predicted correctly. It is one of the simplest form of evaluation metrics. 
The accuracy score is # of correct points / # total = (30 + 35 + 50) / 200 = 
0.575.</p><h3 id='precision' class='header-anchors'>Precision</h3><p>Precision 
is a metric for binary classifier which measures the correctness among all 
positive labels. A binary classifier gives only two output values (i.e. 
positive and ne
 gative). For problem where there are multiple values (3 in our example), we 
first have to transform our problem into a binary classification problem. For 
example, we can have problem whether label = 1.0. The confusion matrix now 
becomes:</p> <table><thead> <tr> <th style="text-align: center"></th> <th 
style="text-align: center">Actual = 1.0</th> <th style="text-align: 
center">Actual != 1.0</th> </tr> </thead><tbody> <tr> <td style="text-align: 
center"><strong>Predicted = 1.0</strong></td> <td style="text-align: 
center">30</td> <td style="text-align: center">60</td> </tr> <tr> <td 
style="text-align: center"><strong>Predicted != 1.0</strong></td> <td 
style="text-align: center">15</td> <td style="text-align: center">95</td> </tr> 
</tbody></table> <p>Precision is the ratio between the number of correct 
positive answer (true positive) and the sum of correct positive answer (true 
positive) and wrong but positively labeled answer (false positive). In this 
case, the precision is 30 / (30 + 
 60) = ~0.3333.</p><h3 id='recall' class='header-anchors'>Recall</h3><p>Recall 
is a metric for binary classifier which measures how many positive labels are 
successfully predicted amongst all positive labels. Formally, it is the ratio 
between the number of correct positive answer (true positive) and the sum of 
correct positive answer (true positive) and wrongly negatively labeled answer 
(false negative). In this case, the recall is 30 / (30 + 15) = 
~0.6667.</p><p>As we have discussed several common metrics for classification 
problem, we can implement them using the <code>Metric</code> class in <a 
href="/evaluation/metricbuild">the next 
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