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Creating a classification model to filter spam


This scenario applies only to Talend Platform products with Big Data and Talend Data Fabric.

For more technologies supported by Talend, see Talend components.

In this scenario, you create Spark Batch Jobs. The key components to be used are as follows:
  • tModelEncoder: several tModelEncoder components are used to transform given SMS text messages into feature sets.

  • tRandomForestModel: it analyzes the features incoming from tModelEncoder to build a classification model that understands what a junk message or a normal message could look like.

  • tClassify: in a new Job, it applies this classification model to process a new set of SMS text messages to classify the spam and the normal messages. In this scenario, the result of this classification is used to evaluate the accuracy of the model, since the classification of the messages processed by tClassify is already known and explicitly marked.

    Information noteDeprecation: This component is deprecated since the 6.2 General Availability release. Talend recommends you to use the tPredict component.
  • tHDFSConfiguration: this component is used by Spark to connect to the HDFS system where the jar files dependent on the Job are transferred.

    In the Spark Configuration tab in the Run view, define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, you must specify the directory in the file system to which these jar files are transferred so that Spark can access these files:
    • Yarn mode (Yarn client or Yarn cluster):
      • When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.

      • When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.

      • When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab.
      • When using Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
      • When using on-premises distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.

    • Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch.

      If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

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