# tRandomForestModel properties for Apache Spark Batch These properties are used to configure tRandomForestModel running in the Spark Batch Job framework. The Spark Batch tRandomForestModel 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 | 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. | | --- | --- | | 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 trees in the forest | Enter the number of decision trees you want tRandomForestModel to build.

Each decision tree is trained independently using a random sample of
features.

Increasing this number can improve the accuracy by decreasing the
variance in predictions, but will increase the training time. | | Maximum depth of each tree in the
forest | 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. | ## Advanced settings | Subsampling rate | Enter the numeric value to indicate the fraction of the
input dataset used for training each tree in the forest. The default
value *1.0* is recommended, meaning to take the
whole dataset for test. | | --- | --- | | Subset strategy | Select the strategy about how many features should be
considered on each internal node in order to appropriately split this
internal node (actually the training set or subset of a feature on this
node) into smaller subsets. These subsets are used to build child
nodes.

Each strategy takes a different number of features into
account to find the optimal point among these features for split. This
point could be, for example, the age *35* of the
categorical feature *age*.

| | Set Checkpoint Interval | Set the frequency of checkpoints. It is recommended to leave the default
value (10).

Before setting a value for this parameter, activate checkpointing and set
the checkpoint directory in the Spark
Configuration tab of the Run
view.

For further information about checkpointing, see
[Logging and checkpointing the activities of your Apache Spark Job](https://help.qlik.com/talend/en-US/components/7.3/spark-batch/logging-and-checkpointing-activities-of-your-apache-spark-job-spark-batch-component-if-you).

For further information about checkpointing the
activities of your Apache Spark Job, see the documentation on Talend Help Center ([https://help.talend.com](https://help.talend.com/)). | | Max bins | 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. | | Min info 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. | | Min 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. | | Impurity | Select the measure used to select the best split from
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. | | Set a random seed | Enter the random seed number to be used for bootstrapping and choosing feature
subsets | ## Usage | **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/en-US/components/7.3/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. |