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.

ElasticNet mixing parameter

Enter the ElasticNet coefficient (numerical value) used for the
regularization calculation in order to control the bias/variance
trade-off in feature selection. ElasticNet is the combination of L1
regularization and L2 regularization.

The value to be put varies between 0.0 and 1.0, indicating the weights
of the L1 regularization and the L2 regularization in the ElasticNet
combination. When the value is 0.0, the regularization is actually
equivalent to the L2 regularization; when the value is 1.0, it is
equivalent to the L1 regularization.

For further information about how ElasticNet is implemented in Spark,
see ML linear methods, in which the related formula shows how the value you put (α in that formula) is used to calculate the
ElasticNet regularization.

For further information about ElasticNet, see Regularization and variable selection via the
elastic net.

Fit an intercept term

Select this check box to allow the tLogisticRegressionModel to automatically calculate the
intercept constants and include them in the regression
computation.

In general, intercept should present to guarantee that the residuals
of your model have a mean of zero.

Maximum number of iterations

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

Regularization

Enter the regularization coefficient (numerical value) to be used
along with ElasticNet for the regularization calculation.

For further information about how this parameter is implemented in
Spark, see ML linear methods, in which the related formula shows how the
value you put (λ in that formula) is
used to calculate the eventual regularization.

Threshold

Enter the threshold (numerical value and ranging between 0.0 and 1.0)
used to separate the positive predictions from the negative predictions.
An element about which the prediction score (the odds of being a case)
is greater or equal to this threshold will be identified as positive,
and negative otherwise.

The default threshold is 0.5.

Convergence tolerance

Enter the convergence score which the iterations are expected to
obtain.

In general, smaller value will result in higher accuracy in the
prediction at the cost of more iterations.

But note that in some cases, your model may not be able to reach the
convergence you put despite of whatever number of iterations you want
the Job to perform. This failure to converge might indicate that the
convergence score you use is not realistic to the features you are
processing and therefore, you need to process these features to a
greater degree.

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 number of iterations, the threshold or the convergence tolerance
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.