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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/logistic-regression.adoc" target="_blank">Edit</a> |
| |
| <h1>Logistic Regression</h1> |
| |
| <div id="preamble"> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Binary Logistic Regression is a special type of regression where a binary response variable is related to a set of explanatory variables, which can be discrete and/or continuous. The important point here to note is that in linear regression, the expected values of the response variable are modeled based on a combination of values taken by the predictors. In logistic regression Probability or Odds of the response taking a particular value is modeled based on the combination of values taken by the predictors. In the Apache Ignite ML module it is implemented via LogisticRegressionModel that solves the binary classification problem. It is a linear method with the loss function in the formulation given by the logistic loss:</p> |
| </div> |
| <div class="imageblock"> |
| <div class="content"> |
| <img src="/docs/2.9.0/images/logistic-regression.png" alt="logistic regression"> |
| </div> |
| </div> |
| <div class="paragraph"> |
| <p>For binary classification problems, the algorithm outputs a binary logistic regression model. Given a new data point, denoted by x, the model makes predictions by applying the logistic function:</p> |
| </div> |
| <div class="imageblock"> |
| <div class="content"> |
| <img src="/docs/2.9.0/images/logistic-regression2.png" alt="logistic regression2"> |
| </div> |
| </div> |
| <div class="paragraph"> |
| <p>By default, if <code>f(wTx)>0.5</code> or <code>\mathrm{f}(\wv^T x) > 0.5</code> (Tex formula), the outcome is positive, or negative otherwise. However, unlike linear SVMs, the raw output of the logistic regression model f(z) has a probabilistic interpretation (i.e., the probability that it is positive).</p> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="model">Model</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>The model is represented by the class <code>LogisticRegressionModel</code> and keeps the weight vector. It enables a prediction to be made 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">LogisticRegressionModel</span> <span class="n">mdl</span> <span class="o">=</span> <span class="err">…</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">predict</span><span class="o">(</span><span class="n">observation</span><span class="o">);</span></code></pre> |
| </div> |
| </div> |
| <div class="paragraph"> |
| <p>Ignite supports several parameters for LogisticRegressionModel:</p> |
| </div> |
| <div class="ulist"> |
| <ul> |
| <li> |
| <p><code>isKeepingRawLabels</code> - controls the output label format: 0 and 1 for false value and raw distances from the separating hyperplane otherwise (default value: false)</p> |
| </li> |
| <li> |
| <p><code>threshold</code> - a threshold to assign label ‘1’ to the observation if the raw value is more than this threshold (default value: 0.5)</p> |
| </li> |
| </ul> |
| </div> |
| <div class="listingblock"> |
| <div class="content"> |
| <pre class="rouge highlight"><code data-lang="java"><span class="nc">LogisticRegressionModel</span> <span class="n">mdl</span> <span class="o">=</span> <span class="err">…</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">withRawLabels</span><span class="o">(</span><span class="kc">true</span><span class="o">).</span><span class="na">withThreshold</span><span class="o">(</span><span class="mf">0.5</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> |
| </div> |
| <div class="sect1"> |
| <h2 id="trainer">Trainer</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Trainer of the binary logistic regression model builds a MLP 1-level trainer under the hood.</p> |
| </div> |
| <div class="paragraph"> |
| <p>Ignite supports the following parameters for LogisticRegressionSGDTrainer:</p> |
| </div> |
| <div class="ulist"> |
| <ul> |
| <li> |
| <p>updatesStgy - update strategy</p> |
| </li> |
| <li> |
| <p>maxIterations - max amount of iterations before convergence</p> |
| </li> |
| <li> |
| <p>batchSize - the size of learning batch</p> |
| </li> |
| <li> |
| <p>locIterations - the amount of local iterations of SGD algorithm</p> |
| </li> |
| <li> |
| <p>seed - seed value for internal random purposes to reproduce training results</p> |
| </li> |
| </ul> |
| </div> |
| <div class="paragraph"> |
| <p>Set up the trainer:</p> |
| </div> |
| <div class="listingblock"> |
| <div class="content"> |
| <pre class="rouge highlight"><code data-lang="java"><span class="nc">LogisticRegressionSGDTrainer</span> <span class="n">trainer</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">LogisticRegressionSGDTrainer</span><span class="o">()</span> |
| <span class="o">.</span><span class="na">withUpdatesStgy</span><span class="o">(</span><span class="no">UPDATES_STRATEGY</span><span class="o">)</span> |
| <span class="o">.</span><span class="na">withAmountOfIterations</span><span class="o">(</span><span class="no">MAX_ITERATIONS</span><span class="o">)</span> |
| <span class="o">.</span><span class="na">withAmountOfLocIterations</span><span class="o">(</span><span class="no">BATCH_SIZE</span><span class="o">)</span> |
| <span class="o">.</span><span class="na">withBatchSize</span><span class="o">(</span><span class="no">LOC_ITERATIONS</span><span class="o">)</span> |
| <span class="o">.</span><span class="na">withSeed</span><span class="o">(</span><span class="no">SEED</span><span class="o">);</span> |
| |
| <span class="c1">// Build the model</span> |
| <span class="nc">LogisticRegressionModel</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 <code>LogRegressionMultiClassModel</code> 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/regression/logistic/multiclass/LogRegressionMultiClassClassificationExample.java" target="_blank" rel="noopener">example</a>, 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> |
| |
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