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<a href="/docs/2.10.0/quick-start/dotnet"
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>.NET/C#</a>
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<a href="/docs/2.10.0/quick-start/cpp"
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>C++</a>
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>Python</a>
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<a href="/docs/2.10.0/quick-start/nodejs"
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>Node.JS</a>
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<a href="/docs/2.10.0/quick-start/sql"
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>SQL</a>
</li>
<li>
<a href="/docs/2.10.0/quick-start/php"
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>PHP</a>
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<a href="/docs/2.10.0/quick-start/restapi"
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>REST API</a>
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>Installing Using Docker</a>
</li>
<li>
<a href="/docs/2.10.0/installation/deb-rpm"
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>Installing DEB or RPM package</a>
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<li><a href="/docs/2.10.0/installation/kubernetes/amazon-eks-deployment" class=''>Amazon EKS</a></li>
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<li>
<a href="/docs/2.10.0/installation/vmware-installation"
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<a href="/docs/2.10.0/understanding-configuration"
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>Setting Up</a>
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<li>
<a href="/docs/2.10.0/logging"
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<li>
<a href="/docs/2.10.0/resources-injection"
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<li>
<a href="/docs/2.10.0/starting-nodes" class='' >Starting and Stopping Nodes</a>
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<button type='button' class='group-toggle collapsed '>Clustering<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
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<a href="/docs/2.10.0/clustering/clustering"
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<a href="/docs/2.10.0/clustering/tcp-ip-discovery"
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>TCP/IP Discovery</a>
</li>
<li>
<a href="/docs/2.10.0/clustering/zookeeper-discovery"
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>ZooKeeper Discovery</a>
</li>
<li>
<a href="/docs/2.10.0/clustering/discovery-in-the-cloud"
class=''
>Discovery in the Cloud</a>
</li>
<li>
<a href="/docs/2.10.0/clustering/network-configuration"
class=''
>Network Configuration</a>
</li>
<li>
<a href="/docs/2.10.0/clustering/connect-client-nodes"
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>Connecting Client Nodes</a>
</li>
<li>
<a href="/docs/2.10.0/clustering/baseline-topology"
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>Baseline Topology</a>
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<li>
<a href="/docs/2.10.0/clustering/running-client-nodes-behind-nat"
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<button type='button' class='group-toggle collapsed '>Thin Clients<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
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<a href="/docs/2.10.0/thin-clients/getting-started-with-thin-clients"
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<li>
<a href="/docs/2.10.0/thin-clients/java-thin-client"
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>Java Thin Client</a>
</li>
<li>
<a href="/docs/2.10.0/thin-clients/dotnet-thin-client"
class=''
>.NET Thin Client</a>
</li>
<li>
<a href="/docs/2.10.0/thin-clients/cpp-thin-client"
class=''
>C++ Thin Client</a>
</li>
<li>
<a href="/docs/2.10.0/thin-clients/python-thin-client"
class=''
>Python Thin Client</a>
</li>
<li>
<a href="/docs/2.10.0/thin-clients/php-thin-client"
class=''
>PHP Thin Client</a>
</li>
<li>
<a href="/docs/2.10.0/thin-clients/nodejs-thin-client"
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>Node.js Thin Client</a>
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<button
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<li><a href="/docs/2.10.0/binary-client-protocol/binary-client-protocol" class=''>Binary Client Protocol</a></li>
<li><a href="/docs/2.10.0/binary-client-protocol/data-format" class=''>Data Format</a></li>
<li><a href="/docs/2.10.0/binary-client-protocol/key-value-queries" class=''>Key-Value Queries</a></li>
<li><a href="/docs/2.10.0/binary-client-protocol/sql-and-scan-queries" class=''>SQL and Scan Queries</a></li>
<li><a href="/docs/2.10.0/binary-client-protocol/binary-type-metadata" class=''>Binary Types Metadata</a></li>
<li><a href="/docs/2.10.0/binary-client-protocol/cache-configuration" class=''>Cache Configuration</a></li>
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</nav>
</li>
<li>
<button type='button' class='group-toggle collapsed '>Data Modeling<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
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<li>
<a href="/docs/2.10.0/data-modeling/data-modeling"
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>Introduction</a>
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<li>
<a href="/docs/2.10.0/data-modeling/data-partitioning"
class=''
>Data Partitioning</a>
</li>
<li>
<a href="/docs/2.10.0/data-modeling/affinity-collocation"
class=''
>Affinity Colocation</a>
</li>
<li>
<a href="/docs/2.10.0/data-modeling/binary-marshaller"
class=''
>Binary Marshaller</a>
</li>
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</li>
<li>
<button type='button' class='group-toggle collapsed '>Configuring Memory<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
<nav class='nav-group collapsed'>
<li>
<a href="/docs/2.10.0/memory-architecture"
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>Memory Architecture</a>
</li>
<li>
<a href="/docs/2.10.0/memory-configuration/data-regions"
class=''
>Configuring Data Regions</a>
</li>
<li>
<a href="/docs/2.10.0/memory-configuration/eviction-policies"
class=''
>Eviction Policies</a>
</li>
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</li>
<li>
<button type='button' class='group-toggle collapsed '>Configuring Persistence<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
<nav class='nav-group collapsed'>
<li>
<a href="/docs/2.10.0/persistence/native-persistence"
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>Ignite Persistence</a>
</li>
<li>
<a href="/docs/2.10.0/persistence/external-storage"
class=''
>External Storage</a>
</li>
<li>
<a href="/docs/2.10.0/persistence/swap"
class=''
>Swapping</a>
</li>
<li>
<a href="/docs/2.10.0/persistence/custom-cache-store"
class=''
>Implementing Custom Cache Store</a>
</li>
<li>
<a href="/docs/2.10.0/persistence/snapshots"
class=''
>Cluster Snapshots</a>
</li>
<li>
<a href="/docs/2.10.0/persistence/disk-compression"
class=''
>Disk Compression</a>
</li>
<li>
<a href="/docs/2.10.0/persistence/persistence-tuning"
class=''
>Tuning Persistence</a>
</li>
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</li>
<li>
<button type='button' class='group-toggle collapsed '>Configuring Caches<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
<nav class='nav-group collapsed'>
<li>
<a href="/docs/2.10.0/configuring-caches/configuration-overview"
class=''
>Cache Configuration</a>
</li>
<li>
<a href="/docs/2.10.0/configuring-caches/configuring-backups"
class=''
>Configuring Partition Backups</a>
</li>
<li>
<a href="/docs/2.10.0/configuring-caches/partition-loss-policy"
class=''
>Partition Loss Policy</a>
</li>
<li>
<a href="/docs/2.10.0/configuring-caches/atomicity-modes"
class=''
>Atomicity Modes</a>
</li>
<li>
<a href="/docs/2.10.0/configuring-caches/expiry-policies"
class=''
>Expiry Policy</a>
</li>
<li>
<a href="/docs/2.10.0/configuring-caches/on-heap-caching"
class=''
>On-Heap Caching</a>
</li>
<li>
<a href="/docs/2.10.0/configuring-caches/cache-groups"
class=''
>Cache Groups</a>
</li>
<li>
<a href="/docs/2.10.0/configuring-caches/near-cache"
class=''
>Near Caches</a>
</li>
</nav>
</li>
<li>
<a href="/docs/2.10.0/data-rebalancing" class='' >Data Rebalancing</a>
</li>
<li>
<a href="/docs/2.10.0/data-streaming" class='' >Data Streaming</a>
</li>
<li>
<button type='button' class='group-toggle collapsed '>Using Key-Value API<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
<nav class='nav-group collapsed'>
<li>
<a href="/docs/2.10.0/key-value-api/basic-cache-operations"
class=''
>Basic Cache Operations</a>
</li>
<li>
<a href="/docs/2.10.0/key-value-api/binary-objects"
class=''
>Working with Binary Objects</a>
</li>
<li>
<a href="/docs/2.10.0/key-value-api/using-scan-queries"
class=''
>Using Scan Queries</a>
</li>
<li>
<a href="/docs/2.10.0/read-repair"
class=''
>Read Repair</a>
</li>
</nav>
</li>
<li>
<a href="/docs/2.10.0/key-value-api/transactions" class='' >Performing Transactions</a>
</li>
<li>
<button type='button' class='group-toggle collapsed '>Working with SQL<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
<nav class='nav-group collapsed'>
<li>
<a href="/docs/2.10.0/SQL/sql-introduction"
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>Introduction</a>
</li>
<li>
<a href="/docs/2.10.0/SQL/schemas"
class=''
>Understanding Schemas</a>
</li>
<li>
<a href="/docs/2.10.0/SQL/indexes"
class=''
>Defining Indexes</a>
</li>
<li>
<a href="/docs/2.10.0/SQL/sql-api"
class=''
>Using SQL API</a>
</li>
<li>
<a href="/docs/2.10.0/SQL/distributed-joins"
class=''
>Distributed Joins</a>
</li>
<li>
<a href="/docs/2.10.0/SQL/sql-transactions"
class=''
>SQL Transactions</a>
</li>
<li>
<a href="/docs/2.10.0/SQL/custom-sql-func"
class=''
>Custom SQL Functions</a>
</li>
<li>
<a href="/docs/2.10.0/SQL/JDBC/jdbc-driver"
class=''
>JDBC Driver</a>
</li>
<li>
<a href="/docs/2.10.0/SQL/JDBC/jdbc-client-driver"
class=''
>JDBC Client Driver</a>
</li>
<li>
<button
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<li><a href="/docs/2.10.0/SQL/ODBC/odbc-driver" class=''>ODBC Driver</a></li>
<li><a href="/docs/2.10.0//SQL/ODBC/connection-string-dsn" class=''>Connection String and DSN</a></li>
<li><a href="/docs/2.10.0/SQL/ODBC/querying-modifying-data" class=''>Querying and Modifying Data</a></li>
<li><a href="/docs/2.10.0/SQL/ODBC/specification" class=''>Specification</a></li>
<li><a href="/docs/2.10.0/SQL/ODBC/data-types" class=''>Data Types</a></li>
<li><a href="/docs/2.10.0/SQL/ODBC/error-codes" class=''>Error Codes</a></li>
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</li>
<li>
<a href="/docs/2.10.0/transactions/mvcc"
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<button type='button' class='group-toggle collapsed '>SQL Reference<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
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<a href="/docs/2.10.0/sql-reference/sql-conformance"
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>SQL Conformance</a>
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<li>
<a href="/docs/2.10.0/sql-reference/ddl"
class=''
>Data Definition Language (DDL)</a>
</li>
<li>
<a href="/docs/2.10.0/sql-reference/dml"
class=''
>Data Manipulation Language (DML)</a>
</li>
<li>
<a href="/docs/2.10.0/sql-reference/transactions"
class=''
>Transactions</a>
</li>
<li>
<a href="/docs/2.10.0/sql-reference/operational-commands"
class=''
>Operational Commands</a>
</li>
<li>
<a href="/docs/2.10.0/sql-reference/aggregate-functions"
class=''
>Aggregate functions</a>
</li>
<li>
<a href="/docs/2.10.0/sql-reference/numeric-functions"
class=''
>Numeric Functions</a>
</li>
<li>
<a href="/docs/2.10.0/sql-reference/string-functions"
class=''
>String Functions</a>
</li>
<li>
<a href="/docs/2.10.0/sql-reference/date-time-functions"
class=''
>Data and Time Functions</a>
</li>
<li>
<a href="/docs/2.10.0/sql-reference/system-functions"
class=''
>System Functions</a>
</li>
<li>
<a href="/docs/2.10.0/sql-reference/data-types"
class=''
>Data Types</a>
</li>
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</li>
<li>
<button type='button' class='group-toggle collapsed '>Distributed Computing<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
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<li>
<a href="/docs/2.10.0/distributed-computing/distributed-computing"
class=''
>Distributed Computing API</a>
</li>
<li>
<a href="/docs/2.10.0/distributed-computing/cluster-groups"
class=''
>Cluster Groups</a>
</li>
<li>
<a href="/docs/2.10.0/distributed-computing/executor-service"
class=''
>Executor Service</a>
</li>
<li>
<a href="/docs/2.10.0/distributed-computing/map-reduce"
class=''
>MapReduce API</a>
</li>
<li>
<a href="/docs/2.10.0/distributed-computing/load-balancing"
class=''
>Load Balancing</a>
</li>
<li>
<a href="/docs/2.10.0/distributed-computing/fault-tolerance"
class=''
>Fault Tolerance</a>
</li>
<li>
<a href="/docs/2.10.0/distributed-computing/job-scheduling"
class=''
>Job Scheduling</a>
</li>
<li>
<a href="/docs/2.10.0/distributed-computing/collocated-computations"
class=''
>Colocating Computations with Data</a>
</li>
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</li>
<li>
<button type='button' class='group-toggle collapsed '>Code Deployment<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
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<li>
<a href="/docs/2.10.0/code-deployment/deploying-user-code"
class=''
>Deploying User Code</a>
</li>
<li>
<a href="/docs/2.10.0/code-deployment/peer-class-loading"
class=''
>Peer Class Loading</a>
</li>
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</li>
<li>
<button type='button' class='group-toggle expanded '>Machine Learning<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
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<li>
<a href="/docs/2.10.0/machine-learning/machine-learning"
class=''
>Machine Learning</a>
</li>
<li>
<a href="/docs/2.10.0/machine-learning/partition-based-dataset"
class=''
>Partition Based Dataset</a>
</li>
<li>
<a href="/docs/2.10.0/machine-learning/updating-trained-models"
class=''
>Updating Trained Models</a>
</li>
<li>
<button
type='button'
class='collapsed '>Binary Classification<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
<nav class="sub_pages collapsed">
<li><a href="/docs/2.10.0/machine-learning/binary-classification/introduction" class=''>Introduction</a></li>
<li><a href="/docs/2.10.0/machine-learning/binary-classification/linear-svm" class=''>Linear SVM (Support Vector Machine)</a></li>
<li><a href="/docs/2.10.0/machine-learning/binary-classification/decision-trees" class=''>Decision Trees</a></li>
<li><a href="/docs/2.10.0/machine-learning/binary-classification/multilayer-perceptron" class=''>Multilayer Perceptron</a></li>
<li><a href="/docs/2.10.0/machine-learning/binary-classification/logistic-regression" class=''>Logistic Regression</a></li>
<li><a href="/docs/2.10.0/machine-learning/binary-classification/knn-classification" class=''>k-NN Classification</a></li>
<li><a href="/docs/2.10.0/machine-learning/binary-classification/ann" class=''>ANN (Approximate Nearest Neighbor)</a></li>
<li><a href="/docs/2.10.0/machine-learning/binary-classification/naive-bayes" class=''>Naive Bayes</a></li>
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</li>
<li>
<button
type='button'
class='expanded parent '>Regression<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
<nav class="sub_pages expanded parent">
<li><a href="/docs/2.10.0/machine-learning/regression/introduction" class=''>Introduction</a></li>
<li><a href="/docs/2.10.0/machine-learning/regression/linear-regression" class='active'>Linear Regression</a></li>
<li><a href="/docs/2.10.0/machine-learning/regression/decision-trees-regression" class=''>Decision Trees Regression</a></li>
<li><a href="/docs/2.10.0/machine-learning/regression/knn-regression" class=''>k-NN Regression</a></li>
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</li>
<li>
<button
type='button'
class='collapsed '>Clustering<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
<nav class="sub_pages collapsed">
<li><a href="/docs/2.10.0/machine-learning/clustering/introduction" class=''>Introduction</a></li>
<li><a href="/docs/2.10.0/machine-learning/clustering/k-means-clustering" class=''>K-Means Clustering</a></li>
<li><a href="/docs/2.10.0/machine-learning/clustering/gaussian-mixture" class=''>Gaussian mixture (GMM)</a></li>
</nav>
</li>
<li>
<a href="/docs/2.10.0/machine-learning/preprocessing"
class=''
>Preprocessing</a>
</li>
<li>
<button
type='button'
class='collapsed '>Model Selection<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
<nav class="sub_pages collapsed">
<li><a href="/docs/2.10.0/machine-learning/model-selection/introduction" class=''>Introduction</a></li>
<li><a href="/docs/2.10.0/machine-learning/model-selection/evaluator" class=''>Evaluator</a></li>
<li><a href="/docs/2.10.0/machine-learning/model-selection/split-the-dataset-on-test-and-train-datasets" class=''>Split the dataset on test and train datasets</a></li>
<li><a href="/docs/2.10.0/machine-learning/model-selection/hyper-parameter-tuning" class=''>Hyper-parameter tuning</a></li>
<li><a href="/docs/2.10.0/machine-learning/model-selection/pipeline-api" class=''>Pipeline API</a></li>
</nav>
</li>
<li>
<a href="/docs/2.10.0/machine-learning/multiclass-classification"
class=''
>Multiclass Classification</a>
</li>
<li>
<button
type='button'
class='collapsed '>Ensemble Methods<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
<nav class="sub_pages collapsed">
<li><a href="/docs/2.10.0/machine-learning/ensemble-methods/introduction" class=''></a></li>
<li><a href="/docs/2.10.0/machine-learning/ensemble-methods/stacking" class=''>Stacking</a></li>
<li><a href="/docs/2.10.0/machine-learning/ensemble-methods/baggin" class=''>Bagging</a></li>
<li><a href="/docs/2.10.0/machine-learning/ensemble-methods/random-forest" class=''>Random Forest</a></li>
<li><a href="/docs/2.10.0/machine-learning/ensemble-methods/gradient-boosting" class=''>Gradient Boosting</a></li>
</nav>
</li>
<li>
<a href="/docs/2.10.0/machine-learning/recommendation-systems"
class=''
>Recommendation Systems</a>
</li>
<li>
<button
type='button'
class='collapsed '>Importing Model<img class="state-indicator" src="/assets/images/left-nav-arrow.svg" width="6" height="10"></button>
<nav class="sub_pages collapsed">
<li><a href="/docs/2.10.0/machine-learning/importing-model/introduction" class=''>Introduction</a></li>
<li><a href="/docs/2.10.0/machine-learning/importing-model/model-import-from-gxboost" class=''>Import Model from XGBoost</a></li>
<li><a href="/docs/2.10.0/machine-learning/importing-model/model-import-from-apache-spark" class=''>Import Model from Apache Spark</a></li>
</nav>
</li>
</nav>
</li>
<li>
<a href="/docs/2.10.0/key-value-api/continuous-queries" class='' >Using Continuous Queries</a>
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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/regression/linear-regression.adoc" target="_blank">Edit</a>
<h1>Linear Regression</h1>
<div class="sect1">
<h2 id="overview">Overview</h2>
<div class="sectionbody">
<div class="paragraph">
<p>Apache Ignite supports the ordinary least squares Linear Regression algorithm - one of the most basic and powerful machine learning algorithms. This documentation describes how the algorithm works, and is implemented in Apache Ignite.</p>
</div>
<div class="paragraph">
<p>The basic idea behind the Linear Regression algorithm is an assumption that a dependent variable <code>y</code> and an explanatory variable <code>x</code> are in the following relationship:</p>
</div>
<div class="imageblock">
<div class="content">
<img src="/docs/2.10.0/images/111.gif" alt="111">
</div>
</div>
<div class="paragraph">
<p>WARNING:Be aware that further documentation uses a dot product of vectors <code>x</code> and <code>b</code>, and explicitly avoids using a constant term. It is mathematically correct in the case where vector <code>x</code> is supplemented by one value equal to 1.</p>
</div>
<div class="paragraph">
<p>The above assumption allows us to make a prediction based on a feature vector <code>x</code> if a vector <code>b</code> is known. This fact is reflected in Apache Ignite in the <code>LinearRegressionModel</code> class responsible for making predictions.</p>
</div>
</div>
</div>
<div class="sect1">
<h2 id="model">Model</h2>
<div class="sectionbody">
<div class="paragraph">
<p>A Model in the case of linear regression is represented by the class <code>LinearRegressionModel</code>. 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">LinearRegressionModel</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>Model is fully independent object and after the training it can be saved, serialized and restored.</p>
</div>
</div>
</div>
<div class="sect1">
<h2 id="trainers">Trainers</h2>
<div class="sectionbody">
<div class="paragraph">
<p>Linear Regression is a supervised learning algorithm. This means that to find parameters (vector <code>b</code>), we need to train on a training dataset and minimize the loss function:</p>
</div>
<div class="imageblock">
<div class="content">
<img src="/docs/2.10.0/images/222.gif" alt="222">
</div>
</div>
<div class="paragraph">
<p>Apache Ignite provides two linear regression trainers: trainer based on the LSQR algorithm and another trainer based on the Stochastic Gradient Descent method.</p>
</div>
<div class="sect2">
<h3 id="lsqr-trainer">LSQR Trainer</h3>
<div class="paragraph">
<p>The LSQR algorithm finds the least-squares solution to a large, sparse, linear system of equations. The Apache Ignite implementation is a distributed version of this algorithm.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="rouge highlight"><code data-lang="java"><span class="c1">// Create linear regression trainer.</span>
<span class="nc">LinearRegressionLSQRTrainer</span> <span class="n">trainer</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">LinearRegressionLSQRTrainer</span><span class="o">();</span>
<span class="c1">// Train model.</span>
<span class="nc">LinearRegressionModel</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>
<span class="c1">// Make a prediction.</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">coordinates</span><span class="o">);</span></code></pre>
</div>
</div>
</div>
<div class="sect2">
<h3 id="sgd-trainer">SGD Trainer</h3>
<div class="paragraph">
<p>Another Linear Regression Trainer uses the stochastic gradient descent method to find a minimum of the loss function. The configuration of this trainer is similar to <a href="/docs/2.10.0/machine-learning/binary-classification/multilayer-perceptron">multilayer perceptron trainer</a> configuration and we can specify the type of updater (<code>SGD</code>, <code>RProp</code> of <code>Nesterov</code>), max number of iterations, batch size, number of local iterations and seed.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="rouge highlight"><code data-lang="java"><span class="c1">// Create linear regression trainer.</span>
<span class="nc">LinearRegressionSGDTrainer</span><span class="o">&lt;?&gt;</span> <span class="n">trainer</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">LinearRegressionSGDTrainer</span><span class="o">&lt;&gt;(</span>
<span class="k">new</span> <span class="nc">UpdatesStrategy</span><span class="o">&lt;&gt;(</span>
<span class="k">new</span> <span class="nf">RPropUpdateCalculator</span><span class="o">(),</span>
<span class="nl">RPropParameterUpdate:</span><span class="o">:</span><span class="n">sumLocal</span><span class="o">,</span>
<span class="nl">RPropParameterUpdate:</span><span class="o">:</span><span class="n">avg</span>
<span class="o">),</span>
<span class="mi">100000</span><span class="o">,</span> <span class="c1">// Max iterations.</span>
<span class="mi">10</span><span class="o">,</span> <span class="c1">// Batch size.</span>
<span class="mi">100</span><span class="o">,</span> <span class="c1">// Local iterations.</span>
<span class="mi">123L</span> <span class="c1">// Random seed.</span>
<span class="o">);</span>
<span class="c1">// Train model.</span>
<span class="nc">LinearRegressionModel</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>
<span class="c1">// Make a prediction.</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">coordinates</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 Linear Regression can be used in practice, try these <a href="https://github.com/apache/ignite/tree/master/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear">examples</a> that are available on GitHub and delivered with every Apache Ignite distribution.</p>
</div>
</div>
</div>
<div class="copyright">
© 2021 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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<li><a href="#trainers">Trainers</a>
<ul class="sectlevel2">
<li><a href="#lsqr-trainer">LSQR Trainer</a></li>
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