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 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.

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.

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
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
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
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 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.

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.

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.

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

Spark Connection

This connection is effective on a per-Job basis.