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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><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 
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 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 [[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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Started</span></a><ul><li class="level-2"><a class="final" 
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class="final" href="/install/"><span>Installing Apache 
PredictionIO</span></a></li><li class="level-2"><a class="final" 
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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/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>Colle
 cting 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(s)</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" 
href="/evaluation/metricchoose/"><
 span>Choosing Evaluation 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 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 
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/recommendati
 on/batch-evaluator/"><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>Classificati
 on</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="expa
 ndible" 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="fi
 nal" href="/community/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="#">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></d
 iv></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 
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 co
 de. 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]], [[PredictedRes
 ult]], 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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@@ -148,4 +148,4 @@ Metrics:
 
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@@ -1,6 +1,6 @@
-<!DOCTYPE html><html><head><title>Choosing Evaluation Metrics</title><meta 
charset="utf-8"/><meta content="IE=edge,chrome=1" 
http-equiv="X-UA-Compatible"/><meta name="viewport" 
content="width=device-width, initial-scale=1.0"/><meta class="swiftype" 
name="title" data-type="string" content="Choosing Evaluation Metrics"/><link 
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PredictionIO" id="logo" 
src="/images/logos/logo-ee2b9bb3.png"/></a><span>™</span></div><div 
id="menu-wrapper"><div id="pill-wrapper"><a class="pill left" 
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SOURCE</a></div></div><img class="mobile-search-bar-toggler hidden-md 
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id="mobile-page-heading-wrapper"><p>PredictionIO Docs</p><h4>Choosing 
Evaluation Metrics</h4></div><h4 class="hidden-sm hidden-xs">PredictionIO 
Docs</h4></div><div class="col-md-3 col-sm-1 col-xs-1 hidden-md hidden-lg"><img 
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class="row"><div i
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class="level-1"><a class="expandible" href="/"><span>Apache PredictionIO™ 
Documentation</span></a><ul><li class="level-2"><a class="final" 
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" 
href="#"><span>Integrating with Your App</span></a><ul><li c
 lass="level-2"><a class="final" href="/appintegration/"><span>App Integration 
Overview</span></a></li><li class="level-2"><a class="expandible" 
href="/sdk/"><span>List of SDKs</span></a><ul><li class="level-3"><a 
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 
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/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>Colle
 cting 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(s)</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/metriccho
 ose/"><span>Choosing 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 
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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/incubator-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="#">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 skimmmed through the process of choosing the 
right <code>Metric</code> in previous sections.</p><p>This secion 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 engine&#39;s goal.</p><p>In the classificaiton 
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 acutal 
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 outpu
 t values (i.e. positive and negative). For problem where there are multiple 
values (3 in our example), we first have to tranform 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 asnwer (false negative). In this case, 
the recall is 30 / (30 + 15) = ~0.6667.</p><p>As we have discussed several 
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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">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/incubator-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="#">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 quali
 ty of an engine variant. We have skimmmed through the process of choosing the 
right <code>Metric</code> in previous sections.</p><p>This secion 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 engine&#39;s goal.</p><p>In the classificaiton 
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 acutal 
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 ou
 tput values (i.e. positive and negative). For problem where there are multiple 
values (3 in our example), we first have to tranform 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 ca
 se, 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 asnwer (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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