July 30, 2023

tGradientBoostedTreeModel – Docs for ESB 7.x


Analyzes feature vectors usually prepared and provided by tModelEncoder to generate a classifier model that is used by tPredict to classify given elements.

tGradientBoostedTreeModel analyzes incoming datasets using the Decision
Tree algorithm.

It generates a binary classification model out of this analysis and writes
this model either in memory or in a given file system.

tGradientBoostedTreeModel properties for Apache Spark Batch

These properties are used to configure tGradientBoostedTreeModel running in the Spark Batch Job framework.

The Spark Batch
tGradientBoostedTreeModel component belongs to the Machine Learning family.

This component is available in Talend Platform products with Big Data and
in Talend Data Fabric.

Basic settings

Schema and Edit schema

A schema is a row description. It defines the number of fields
(columns) to be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the

Click Edit
to make changes to the schema. If the current schema is of the Repository type, three options are available:

  • View schema: choose this
    option to view the schema only.

  • Change to built-in property:
    choose this option to change the schema to Built-in for local changes.

  • Update repository connection:
    choose this option to change the schema stored in the repository and decide whether
    to propagate the changes to all the Jobs upon completion. If you just want to
    propagate the changes to the current Job, you can select No upon completion and choose this schema metadata
    again in the Repository Content

Label column

Select the input column used to provide classification labels. The records of this column
are used as the class names (Target in terms of classification) of the elements to be

This component supports only binary classification model. If you need to create a multi-class model, use tDecisionTreeModel or tRandomForestModel.

Feature column

Select the input column used to provide features. Very often, this column is the output of
the feature engineering computations performed by tModelEncoder.

Save the model on file

Select this check box to store the model in a given file system. Otherwise, the model is
stored in memory. The button for browsing does not work with the Spark Local mode; if you are using the Spark Yarn
or the Spark Standalone mode, ensure that you have properly
configured the connection in a configuration component in the same Job, such as tHDFSConfiguration.

Number of iterations

Enter the number of iterations you want the Job to perform to train the model.

Subsampling rate

Enter the numeric value to indicate the fraction of the input dataset used for
each iteration. The default value 1.0d is recommended, meaning to take the whole dataset for

Gain calculation method

An information gain is expected each time a node split occurs. From this drop-down list,
select the measure used to define the best split out of each set of splits.

  • gini: it is about how often an element could be
    incorrectly labelled in a split.

  • entropy: it is about how unpredictable the information in
    each split is.

For further information about how each of the measures is calculated, see Impurity measures from the Spark documentation.

Maximum number of bins used for descritizing
continuous features

Enter the numeric value to indicate the maximum number of bins used for splitting

The continuous features are automatically transformed to ordered discrete features.

Maximum depth of the tree

Enter the decision tree depth at which the training should stop adding new nodes. New
nodes represent further tests on features on internal nodes and possible class labels held
by leaf nodes.

For a tree of n depth, the number of internal nodes is
2n – 1. For example, depth
1 means 1 internal
node plus 2 leaf nodes.

Generally speaking, a deeper decision tree is more expressive and thus potentially more
accurate in predictions, but it is also more resource consuming and prone to

Minimum information gain

Enter the minimum number of information gain to be expected from a parent node to its
child nodes. When the number of information gain is less than this minimum number, node
split is stopped.

The default value of the minimum number of information gain is 0.0, meaning that no further information is obtained by splitting a given node.
As a result, the splitting could be stopped.

For further information about how the information gain is calculated, see Impurity and Information gain from the Spark documentation.

Minimum number of instances per

Enter the minimum number of training instances a node should have to make it valid for
further splitting.

The default value is 1, which means when a node has
only 1 row of training data, it stops splitting.

Advanced settings

Maximum memory

Enter the maximum amount of memory (in MB) to be allocated to the training of the

Checkpoint interval

Enter a number to indicate the checkpoint frequency. Every time at the end of the
execution of this number of iterations the temporary model is saved.

Define the random seed

Enter the random seed number to be used for bootstrapping and choosing feature


Usage rule

This component is used as an end component and requires an input link.

You can accelerate the training process by adjusting the stopping conditions such as the
maximum depth of each decision tree, the maximum number of bins of splitting or the minimum
number of information gain, but note that the training that stops too early could impact its

Model evaluation

The parameters you need to set are free parameters and so their values may be provided by
previous experiments, empirical guesses or the like. They do not have any optimal values
applicable for all datasets.

Therefore, you need to train the classifier model you are generating with different sets
of parameter values until you can obtain the best confusion matrix. But note that you need
to write the evaluation code yourself to rank your model with scores.

You need to select the scores to be used depending on the algorithm you want to use to
train your classifier model. This allows you to build the most relevant confusion

For examples about how the confusion matrix is used in a
Job for classification, see Creating a classification model to filter spam.

For a general explanation about confusion matrix, see https://en.wikipedia.org/wiki/Confusion_matrix from Wikipedia.

Spark Connection

In the Spark
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

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

    • When using Altus, specify the S3 bucket or the Azure
      Data Lake Storage for Job deployment in the Spark
    • 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-premise
      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

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

This connection is effective on a per-Job basis.

Related scenarios

No scenario is available for the Spark Batch version of this component

Document get from Talend https://help.talend.com
Thank you for watching.
Notify of
Inline Feedbacks
View all comments
Would love your thoughts, please comment.x