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| <li> |
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| <a href="/docs/latest/distributed-computing/executor-service" |
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| class='' |
| >Executor Service</a> |
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| <li> |
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| <a href="/docs/latest/distributed-computing/map-reduce" |
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| >MapReduce API</a> |
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| <li> |
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| <a href="/docs/latest/distributed-computing/load-balancing" |
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| class='' |
| >Load Balancing</a> |
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| <li> |
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| <a href="/docs/latest/distributed-computing/fault-tolerance" |
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| class='' |
| >Fault Tolerance</a> |
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| <li> |
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| <a href="/docs/latest/distributed-computing/job-scheduling" |
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| >Job Scheduling</a> |
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| <a href="/docs/latest/collocated-computations" class='' >Colocating Computations with Data</a> |
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| <a href="/docs/latest/events/events" |
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| <li><a href="/docs/latest//machine-learning/clustering/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/latest//machine-learning/clustering/gaussian-mixture" class=''>Gaussian mixture (GMM)</a></li> |
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| <li> |
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| <li><a href="/docs/latest//machine-learning/model-selection/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/latest//machine-learning/model-selection/split-the-dataset-on-test-and-train-datasets" class=''>Split the dataset on test and train datasets</a></li> |
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| <li><a href="/docs/latest//machine-learning/model-selection/hyper-parameter-tuning" class=''>Hyper-parameter tuning</a></li> |
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| <li><a href="/docs/latest//machine-learning/ensemble-methods/introduction" class=''></a></li> |
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| <li><a href="/docs/latest//machine-learning/ensemble-methods/baggin" class=''>Bagging</a></li> |
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| <li><a href="/docs/latest//machine-learning/ensemble-methods/random-forest" class=''>Random Forest</a></li> |
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| <li><a href="/docs/latest//machine-learning/ensemble-methods/gradient-boosting" class=''>Gradient Boosting</a></li> |
| |
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| <li> |
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| <a href="/docs/latest/machine-learning/recommendation-systems" |
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| class='' |
| >Recommendation Systems</a> |
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| <li><a href="/docs/latest//machine-learning/importing-model/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/latest//machine-learning/importing-model/model-import-from-gxboost" class=''>Import Model from XGBoost</a></li> |
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| <li><a href="/docs/latest//machine-learning/importing-model/model-import-from-apache-spark" class=''>Import Model from Apache Spark</a></li> |
| |
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| </nav> |
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| <a href="/docs/latest/monitoring-metrics/intro" |
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| <li> |
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| <a href="/docs/latest/monitoring-metrics/cluster-id" |
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| <li><a href="/docs/latest//monitoring-metrics/configuring-metrics" class=''>Configuring Metrics</a></li> |
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| <li><a href="/docs/latest//monitoring-metrics/metrics" class=''>JMX Metrics</a></li> |
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| >C++ Thin Client</a> |
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| <a href="/docs/latest/thin-clients/python-thin-client" |
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| >Python Thin Client</a> |
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| <li><a href="/docs/latest//binary-client-protocol/binary-client-protocol" class=''>Binary Client Protocol</a></li> |
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| <a href="/docs/latest/SQL/ODBC/odbc-driver" |
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| >ODBC Driver</a> |
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| <li> |
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| <a href="/docs/latest/SQL/ODBC/connection-string-dsn" |
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| class='' |
| >Connection String and DSN</a> |
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| <a href="/docs/latest/SQL/ODBC/querying-modifying-data" |
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| <a href="/docs/latest/SQL/ODBC/specification" |
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| class='' |
| >Specification</a> |
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| <li> |
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| <a href="/docs/latest/SQL/ODBC/data-types" |
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| class='' |
| >Data Types</a> |
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| </li> |
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| <li> |
| |
| <a href="/docs/latest/SQL/ODBC/error-codes" |
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| class='' |
| >Error Codes</a> |
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| |
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| <a href="/docs/latest/restapi" class='' >REST API</a> |
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| <a href="/docs/latest/control-script" class='' >Control Script</a> |
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| <a href="/docs/latest/plugins" class='' >Plugins</a> |
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| <a href="/docs/latest/sqlline" class='' >SQLLine</a> |
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| <button type='button' data-guide-url="" class='group-toggle collapsed '>Ignite for Spark<img class="state-indicator" src="/assets/images/left-nav-arrow.svg"></button> |
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| <article data-swiftype-index='true'> |
| <a class='edit-link' href="https://github.com/apache/ignite/tree/IGNITE-7595/docs/_docs/machine-learning/ensemble-methods/gradient-boosting.adoc" target="_blank">Edit</a> |
| |
| <h1>Gradient Boosting</h1> |
| |
| <div id="preamble"> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones.</p> |
| </div> |
| <div class="admonitionblock note"> |
| <table> |
| <tr> |
| <td class="icon"> |
| <div class="title">Note</div> |
| </td> |
| <td class="content"> |
| <h3 id="question-posed-by-kearns-and-valiant-1988-1989" class="discrete">Question posed by Kearns and Valiant (1988, 1989)</h3> |
| <div class="paragraph"> |
| <p>"Can a set of weak learners create a single strong learner?"</p> |
| </div> |
| <div class="literalblock"> |
| <div class="content"> |
| <pre>A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification.</pre> |
| </div> |
| </div> |
| </td> |
| </tr> |
| </table> |
| </div> |
| <div class="paragraph"> |
| <p>Later, in 1990 it was demonstrated by Robert Schapire and led to the boosting technique development.</p> |
| </div> |
| <div class="paragraph"> |
| <p>The boosing is presented in Ignite ML library as a Gradient Boosting (the most popular boosting implementation).</p> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="overview">Overview</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Gradient boosting is a machine learning technique that produces a prediction model in the form of an <a href="https://en.wikipedia.org/wiki/Ensemble_learning">ensemble</a> of weak prediction models. A gradient boosting algorithm tries to solve the minimization error problem on learning samples in a functional space where each function is a model. Each model in this composition tries to predict a gradient of error for points in a feature space and these predictions will be summed with some weight to model an answer. This algorithm may be used for regression and classification problems. For more information please see <a href="https://en.wikipedia.org/wiki/Gradient_boosting">Wikipedia</a>.</p> |
| </div> |
| <div class="paragraph"> |
| <p>In Ignite ML there is an implementation of a general GDB algorithm and GDB-on-trees algorithm. General GDB (GDBRegressionTrainer and GDBBinaryClassifierTrainer) allows any trainer for training each model in composition. GDB on trees uses some optimizations specific for trees, such as indexes, for avoiding sorting during the decision tree build phase.</p> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="model">Model</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Apache Ignite ML purposes all implementations of the GDB algorithm to use GDBModel, wrapping ModelsComposition for representing the composition of a few models. ModelsComposition implements a common Model interface and can be used as follows:</p> |
| </div> |
| <div class="listingblock"> |
| <div class="content"> |
| <pre class="rouge highlight"><code data-lang="java"><span class="nc">GDBModel</span> <span class="n">model</span> <span class="o">=</span> <span class="o">...;</span> |
| |
| <span class="kt">double</span> <span class="n">prediction</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">predict</span><span class="o">(</span><span class="n">observation</span><span class="o">);</span></code></pre> |
| </div> |
| </div> |
| <div class="paragraph"> |
| <p>GDBModel uses WeightedPredictionsAggregator as the model answer reducer. This aggregator computes an answer of a meta-model, since “result = bias + p1*w1 + p2*w2 + …​” where</p> |
| </div> |
| <div class="ulist"> |
| <ul> |
| <li> |
| <p><code>pi</code> - answer of i-th model.</p> |
| </li> |
| <li> |
| <p><code>wi</code> - weight of model in composition.</p> |
| </li> |
| </ul> |
| </div> |
| <div class="paragraph"> |
| <p>GDB uses the mean value of labels for the bias-parameter in the aggregator.</p> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="trainer">Trainer</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Training of GDB is represented by <code>GDBRegressionTrainer</code>, <code>GDBBinaryClassificationTrainer</code> and <code>GDBRegressionOnTreesTrainer</code>, <code>GDBBinaryClassificationOnTreesTrainer</code> for general GDB and GDB on trees respectively. All trainers have the following parameters:</p> |
| </div> |
| <div class="ulist"> |
| <ul> |
| <li> |
| <p><code>gradStepSize</code> - sets the constant weight of each model in composition; in future versions of Ignite ML this parameter may be computed dynamically.</p> |
| </li> |
| <li> |
| <p><code>cntOfIterations</code> - sets the maximum of models in the composition after training.</p> |
| </li> |
| <li> |
| <p><code>checkConvergenceFactory</code> - sets factory for construction of convergence checker used for preventing overfitting and learning of many useless models while training.</p> |
| </li> |
| </ul> |
| </div> |
| <div class="paragraph"> |
| <p>For classifier trainers there is addition parameter:</p> |
| </div> |
| <div class="ulist"> |
| <ul> |
| <li> |
| <p><code>loss</code> - sets loss computer on some learning example from a training dataset.</p> |
| </li> |
| </ul> |
| </div> |
| <div class="paragraph"> |
| <p>There are several factories for convergence checkers:</p> |
| </div> |
| <div class="ulist"> |
| <ul> |
| <li> |
| <p><code>ConvergenceCheckerStubFactory</code> creates a checker that always returns false for a convergence check. So in this case, model composition size will have cntOfIterations models.</p> |
| </li> |
| <li> |
| <p><code>MeanAbsValueConvergenceCheckerFactory</code> creates a checker that compute a mean value of the absolute gradient values on each example from a dataset and returns true if this it is less than the used-defined threshold.</p> |
| </li> |
| <li> |
| <p><code>MedianOfMedianConvergenceCheckerFactory</code> creates a checker that computes the median of median absolute gradient values on each data partition. This method is less sensitive for anomalies in the learning dataset, but GDB may converge longer.</p> |
| </li> |
| </ul> |
| </div> |
| <div class="paragraph"> |
| <p>Example of training:</p> |
| </div> |
| <div class="listingblock"> |
| <div class="content"> |
| <pre class="rouge highlight"><code data-lang="java"><span class="c1">// Set up trainer</span> |
| <span class="nc">GDBTrainer</span> <span class="n">trainer</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">GDBBinaryClassifierOnTreesTrainer</span><span class="o">(</span> |
| <span class="n">learningRate</span><span class="o">,</span> <span class="n">countOfIterations</span><span class="o">,</span> <span class="k">new</span> <span class="nc">LogLoss</span><span class="o">()</span> |
| <span class="o">).</span><span class="na">withCheckConvergenceStgyFactory</span><span class="o">(</span><span class="k">new</span> <span class="nc">MedianOfMedianConvergenceCheckFactory</span><span class="o">(</span><span class="n">precision</span><span class="o">));</span> |
| |
| <span class="c1">// Build the model</span> |
| <span class="nc">GDBModel</span> <span class="n">mdl</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span> |
| <span class="n">ignite</span><span class="o">,</span> |
| <span class="n">dataCache</span><span class="o">,</span> |
| <span class="n">vectorizer</span> |
| <span class="o">);</span></code></pre> |
| </div> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="example">Example</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>To see how GDB Classifier can be used in practice, try this <a href="https://github.com/apache/ignite/blob/master/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java">example</a> that is available on GitHub and delivered with every Apache Ignite distribution.</p> |
| </div> |
| </div> |
| </div> |
| <div class="copyright"> |
| © 2020 The Apache Software Foundation.<br/> |
| Apache, Apache Ignite, the Apache feather and the Apache Ignite logo are either registered trademarks or trademarks of The Apache Software Foundation. |
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