# tGradientBoostedTreeModel 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. In local mode, Apache Spark 3.0.x and later versions are supported. This component is not shipped with your Talend Studio by default. You need to install it using the Feature Manager. For more information, see [Installing features using the Feature Manager](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/install-features-to-talend-studio). ## 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 | Properties | Description | | --- | --- | | 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
fields.

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

| | 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
classified.

This component supports only binary classification model. If you need to create a multi-class model, use [tDecisionTreeModel](https://help.qlik.com/talend/en-US/components/8.0/machine-learning/tdecisiontreemodel) or [tRandomForestModel](https://help.qlik.com/talend/en-US/components/8.0/machine-learning/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
system | 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
test. | | 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.



For further information about how each of the measures is calculated, see [Impurity measures](http://spark.apache.org/docs/latest/mllib-decision-tree.html#node-impurity-and-information-gain) from the Spark documentation. | | Maximum number of bins used for discretizing
continuous features | Enter the numeric value to indicate the maximum number of bins used for splitting
features.

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
*2^n^ - 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
overfitting. | | 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](http://spark.apache.org/docs/latest/mllib-decision-tree.html#node-impurity-and-information-gain) from the Spark documentation. | | Minimum number of instances per
node | 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 | Properties | Description | | --- | --- | | Maximum memory | Enter the maximum amount of memory (in MB) to be allocated to the training of the
tree. | | 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
subsets | ## Usage | Usage guidance | Description | | --- | --- | | **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
performance. | | 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
matrix.

For examples about how the confusion matrix is used in a
Talend
Job for classification, see [Creating a classification model to filter spam](https://help.qlik.com/talend/r/en-US/8.0/components/machine-learning/classification-model).

For a general explanation about confusion matrix, see https://en.wikipedia.org/wiki/Confusion\_matrix from Wikipedia. | | Spark Connection | 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:



This connection is effective on a per-Job basis. |