Define a storage configuration
component

Select the configuration component to be used to provide the configuration
information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local
system.

The configuration component to be used must be present in the same Job. For
example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write
the result in a given HDFS system.

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. The schema is either Built-In or stored remotely in the Repository.

Click Sync columns to retrieve the schema from
the previous component connected in the Job.

Click Edit schema 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] window.

Built-In: You create and store the
schema locally for this component only. Related topic: see
Talend Studio

User Guide.

Repository: You have already created
the schema and stored it in the Repository. You can reuse it in various projects and
Job designs. Related topic: see
Talend Studio

User Guide.

Matching key

Select the columns on which you want to base the match
computation.

Matching label column

Select the column from the input flow which holds the
label you set manually on the suspect pairs of records.

If you select the Integration with Data
Stewardship check box, this list does not appear. In
this case, the matching label column is the
TDS_ARBITRATION_LEVEL column, which holds the
label(s) you set on the suspect pairs of records set using Talend Data Stewardship.

Matching model location

Select the Save the model on
file system check box and in the Folder field, set the path to the local
folder where you want to generate the matching files.

If you want to store the model in a specific file system,
for example S3 or HDFS, you must use the corresponding component in the
Job and select the Define a storage
configuration component check box in the component basic
settings.

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.

Integration with Data Stewardship

Select this check box to set the connection parameters to the

Talend Data Stewardship
server.

If you select this check box, tMatchModel uses the
sample suspect records labeled in a Grouping campaign defined on the

Talend Data Stewardship
server, which means this component can be used as a
standalone component.

Data Stewardship Configuration

• URL:

  Enter the address to access the Talend Data Stewardship server suffixed
  with /data-stewardship/, for example
  http://<server_address>:19999/data-stewardship/.

• Username and
  Password:

  Enter the authentication information to the Talend Data Stewardship server.

• Campaign Name:

  A read-only field which shows the campaign name once the campaign is
  selected.

  Click Find a Campaign to open a dialog box
  which lists the Grouping campaigns on the server for which you
  are the Campaign owner or you have the access rights.

  Click the refresh button to retrieve the campaign details from
  the
  Talend Data Stewardship
  server.

• Campaign Label:

  A read-only field which shows the campaign label
  once the campaign is selected.

Max token number for phonetic
comparison

Set the maximum number of the tokens to be used in the
phonetic comparison.

When the number of tokens exceeds what has been defined
in this field, no phonetic comparison is done on the string.

Random Forest hyper-parameters
tuning

Number of trees range: Enter a
range for the decision trees you want to build. Each decision tree is
trained independently using a random sample of features.

Increasing this range can improve the accuracy by
decreasing the variance in predictions, but will increase the training
time.

Maximum tree-depth range: Enter a
range for 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.

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.

Cross-validation parameters

Number of folds: Enter a numeric
value of bins which are used as separate training and test datasets.

Evaluation metric type: Select a
type from the list. For further information, see Precision and
recall.

Random Forest parameters

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.

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 from the Spark documentation.

Min instance 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.

• 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.

Set a random seed

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

Data Stewardship Configuration

This field only appears if you selected the Integration with
Data Stewardship check box in the Basic
settings.

Batch Size: Specify the number of records to be
processed in each batch.

Do not change the default value unless you are facing performance issues.
Increasing the batch size can improve the performance but setting a too
high value could cause Job failures.

Usage rule

This component is used as an intermediate step.

This component, along with the Spark Batch component Palette it belongs to, appears only
when you are creating a Spark Batch Job.

Spark Batch Connection

This connection is effective on a per-Job basis.