Creating a classification model to filter spam - 7.0

Machine Learning

author
Talend Documentation Team
EnrichVersion
7.0
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Real-Time Big Data Platform
task
Data Governance > Third-party systems > Machine Learning components
Data Quality and Preparation > Third-party systems > Machine Learning components
Design and Development > Third-party systems > Machine Learning components
EnrichPlatform
Talend Studio

This scenario applies only to subscription-based 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.

  • 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 store (technical preview) for Job deployment in the Spark configuration tab.
      • When using other 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 or tS3Configuration.