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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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| class='' |
| >MapReduce API</a> |
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| </li> |
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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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| <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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| <li> |
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| <a href="/docs/latest/distributed-computing/job-scheduling" |
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| class='' |
| >Job Scheduling</a> |
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| </nav> |
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| <li> |
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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/listening-to-events" |
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| class='' |
| >Enabling and Listenting to Events</a> |
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| <li> |
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| <a href="/docs/latest/events/events" |
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| <a href="/docs/latest/near-cache" class='' >Near Caches</a> |
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| <a href="/docs/latest/platform-cache" class='' >.NET Platform Cache</a> |
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| class='' |
| >Queue and Set</a> |
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| <li> |
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| <a href="/docs/latest/data-structures/atomic-types" |
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| >Atomic Types</a> |
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| <li> |
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| <a href="/docs/latest/data-structures/countdownlatch" |
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| class='' |
| >CountDownLatch</a> |
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| <li> |
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| <a href="/docs/latest/data-structures/atomic-sequence" |
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| <a href="/docs/latest/data-structures/semaphore" |
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| class='' |
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| <li><a href="/docs/latest//machine-learning/regression/knn-regression" class=''>k-NN Regression</a></li> |
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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/k-means-clustering" class=''>K-Means Clustering</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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| <a href="/docs/latest/machine-learning/preprocessing" |
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| class='' |
| >Preprocessing</a> |
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| <li> |
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| class='collapsed '>Model Selection<img class="state-indicator" src="/assets/images/left-nav-arrow.svg"></button> |
| <nav class="sub_pages collapsed"> |
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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/evaluator" class=''>Evaluator</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/model-selection/pipeline-api" class=''>Pipeline API</a></li> |
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| </nav> |
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| <li> |
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| <a href="/docs/latest/machine-learning/multiclass-classification" |
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| class='' |
| >Multiclass Classification</a> |
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| <li> |
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| <button |
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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/stacking" class=''>Stacking</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> |
| |
| </nav> |
| |
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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> |
| |
| </li> |
| |
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| <li> |
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| <button |
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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> |
| |
| </nav> |
| |
| </li> |
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| </nav> |
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| <a href="/docs/latest/monitoring-metrics/intro" |
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| >Introduction</a> |
| |
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| <li> |
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| <a href="/docs/latest/monitoring-metrics/cluster-id" |
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| class='' |
| >Cluster ID and Tag</a> |
| |
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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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| <li><a href="/docs/latest//security/tde" class=''>Introduction</a></li> |
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| >Thin Clients Overview</a> |
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| <a href="/docs/latest/thin-clients/java-thin-client" |
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| >Java Thin Client</a> |
| |
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| <a href="/docs/latest/thin-clients/dotnet-thin-client" |
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| >.NET Thin Client</a> |
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| <li> |
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| <a href="/docs/latest/thin-clients/cpp-thin-client" |
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| >C++ Thin Client</a> |
| |
| </li> |
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| <li> |
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| <a href="/docs/latest/thin-clients/python-thin-client" |
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| class='' |
| >Python Thin Client</a> |
| |
| </li> |
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| <li> |
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| <a href="/docs/latest/thin-clients/php-thin-client" |
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| class='' |
| >PHP Thin Client</a> |
| |
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| <li> |
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| <a href="/docs/latest/thin-clients/nodejs-thin-client" |
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| class='' |
| >Node.js Thin Client</a> |
| |
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| |
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| <button |
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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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| <li><a href="/docs/latest//binary-client-protocol/data-format" class=''>Data Format</a></li> |
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| <li><a href="/docs/latest//binary-client-protocol/key-value-queries" class=''>Key-Value Queries</a></li> |
| |
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| <li><a href="/docs/latest//binary-client-protocol/sql-and-scan-queries" class=''>SQL and Scan Queries</a></li> |
| |
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| <li><a href="/docs/latest//binary-client-protocol/binary-type-metadata" class=''>Binary Types Metadata</a></li> |
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| <li><a href="/docs/latest//binary-client-protocol/cache-configuration" class=''>Cache Configuration</a></li> |
| |
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| <li> |
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| <a href="/docs/latest/SQL/ODBC/odbc-driver" |
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| class='' |
| >ODBC Driver</a> |
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| </li> |
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| |
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| <li> |
| |
| <a href="/docs/latest/SQL/ODBC/connection-string-dsn" |
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| class='' |
| >Connection String and DSN</a> |
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| </li> |
| |
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| |
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| <li> |
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| <a href="/docs/latest/SQL/ODBC/querying-modifying-data" |
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| class='' |
| >Querying and Modifying Data</a> |
| |
| </li> |
| |
| |
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| |
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| <li> |
| |
| <a href="/docs/latest/SQL/ODBC/specification" |
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| class='' |
| >Specification</a> |
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| </li> |
| |
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| |
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| <li> |
| |
| <a href="/docs/latest/SQL/ODBC/data-types" |
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| class='' |
| >Data Types</a> |
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| </li> |
| |
| |
| |
| |
| |
| <li> |
| |
| <a href="/docs/latest/SQL/ODBC/error-codes" |
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| class='' |
| >Error Codes</a> |
| |
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| |
| |
| </nav> |
| |
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| <li> |
| |
| |
| <a href="/docs/latest/restapi" class='' >REST API</a> |
| |
| </li> |
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| <li> |
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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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| <li> |
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| |
| <a href="/docs/latest/sqlline" class='' >SQLLine</a> |
| |
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| |
| <li> |
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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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| <li> |
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| <a href="/docs/latest/ignite-for-spark/overview" |
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| class='' |
| >Overview</a> |
| |
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| |
| |
| |
| <li> |
| |
| <a href="/docs/latest/ignite-for-spark/ignitecontext-and-rdd" |
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| class='' |
| >IgniteContext and IgniteRDD</a> |
| |
| </li> |
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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/binary-classification/decision-trees.adoc" target="_blank">Edit</a> |
| |
| <h1>Decision Trees</h1> |
| |
| <div id="preamble"> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Decision trees and their ensembles are popular methods for the machine learning tasks of classification and regression. Decision trees are widely used since they are easy to interpret, handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture non-linearities and feature interactions. Tree ensemble algorithms such as random forests and boosting are among the top performers for classification and regression tasks.</p> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="overview">Overview</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Decision trees are a simple yet powerful model in supervised machine learning. The main idea is to split a feature space into regions such as that the value in each region varies a little. The measure of the values' variation in a region is called the impurity of the region.</p> |
| </div> |
| <div class="paragraph"> |
| <p>Apache Ignite provides an implementation of the algorithm optimized for data stored in rows (see <a href="/docs/latest/machine-learning/partition-based-dataset">Partition Based Dataset</a>).</p> |
| </div> |
| <div class="paragraph"> |
| <p>Splits are done recursively and every region created from a split can be split further. Therefore, the whole process can be described by a binary tree, where each node is a particular region and its children are the regions derived from it by another split.</p> |
| </div> |
| <div class="paragraph"> |
| <p>Let each sample from a training set belong to some space <code>S</code> and let <code>p_i</code> be a projection on a feature with index <code>i</code>, then a split by continuous feature with index <code>i</code> has the form:</p> |
| </div> |
| <div class="imageblock"> |
| <div class="content"> |
| <img src="/docs/2.9.0/images/555.gif" alt="555"> |
| </div> |
| </div> |
| <div class="paragraph"> |
| <p>and a split by categorical feature with values from some set <code>X</code> has the form:</p> |
| </div> |
| <div class="imageblock"> |
| <div class="content"> |
| <img src="/docs/2.9.0/images/666.gif" alt="666"> |
| </div> |
| </div> |
| <div class="paragraph"> |
| <p>Here <code>X_0</code> is a subset of <code>X</code>.</p> |
| </div> |
| <div class="paragraph"> |
| <p>The model works this way - the split process stops when either the algorithm has reached the configured maximal depth, or splitting of any region has not resulted in significant impurity loss. Prediction of a value for point <code>s</code> from <code>S</code> is a traversal of the tree down to the node that corresponds to the region containing <code>s</code> and getting back a value associated with this leaf.</p> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="model">Model</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>The Model in a decision tree classification is represented by the class <code>DecisionTreeNode</code>. We can make a prediction for a given vector of features in the following way:</p> |
| </div> |
| <div class="listingblock"> |
| <div class="content"> |
| <pre class="rouge highlight"><code data-lang="java"><span class="nc">DecisionTreeNode</span> <span class="n">mdl</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">mdl</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">observation</span><span class="o">);</span></code></pre> |
| </div> |
| </div> |
| <div class="paragraph"> |
| <p>The model is a fully independent object and after the training it can be saved, serialized and restored.</p> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="trainer">Trainer</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>A Decision Tree algorithm can be used for classification and regression depending upon the impurity measure and node instantiation approach.</p> |
| </div> |
| <div class="sect2"> |
| <h3 id="classification">Classification</h3> |
| <div class="paragraph"> |
| <p>The Classification Decision Tree uses the <a href="https://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity">Gini</a> impurity measure and you can use it in the following way:</p> |
| </div> |
| <div class="listingblock"> |
| <div class="content"> |
| <pre class="rouge highlight"><code data-lang="java"><span class="c1">// Create decision tree classification trainer.</span> |
| <span class="nc">DecisionTreeClassificationTrainer</span> <span class="n">trainer</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">DecisionTreeClassificationTrainer</span><span class="o">(</span> |
| <span class="mi">4</span><span class="o">,</span> <span class="c1">// Max deep.</span> |
| <span class="mi">0</span> <span class="c1">// Min impurity decrease.</span> |
| <span class="o">);</span> |
| |
| <span class="c1">// Train model.</span> |
| <span class="nc">DecisionTreeNode</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> |
| <div class="sect1"> |
| <h2 id="examples">Examples</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>To see how the Decision Tree 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/DecisionTreeClassificationTrainerExample.java">classification 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. |
| |
| </div> |
| |
| </article> |
| <nav class="right-nav" data-swiftype-index='false'> |
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| <li><a href="#overview">Overview</a></li> |
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| <li><a href="#trainer">Trainer</a> |
| <ul class="sectlevel2"> |
| <li><a href="#classification">Classification</a></li> |
| </ul> |
| </li> |
| <li><a href="#examples">Examples</a></li> |
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