Analyzes feature vectors usually prepared and provided by tModelEncoder to generate a classifier model that is used by tPredict to classify given elements.
tDecisionTreeModel analyzes incoming
datasets using the Decision Tree algorithm.
It generates a classification model out of this analysis and writes
this model either in memory or in a given file system.
tDecisionTreeModel properties for Apache Spark Batch
These properties are used to configure tDecisionTreeModel running in the Spark Batch Job framework.
The Spark Batch
tDecisionTreeModel component belongs to the Machine Learning family.
Schema and Edit schema
A schema is a row description. It defines the number of fields
Select the input column used to provide classification labels. The records of this column
Select the input column used to provide features. Very often, this column is the output of
Save the model on file
Select this check box to store the model in a given file system. Otherwise, the model is
Gain calculation method
An information gain is expected each time a node split occurs. From this drop-down list,
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
Enter the numeric value to indicate the maximum number of bins used for splitting
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
For a tree of n depth, the number of internal nodes is
Generally speaking, a deeper decision tree is more expressive and thus potentially more
Minimum information gain
Enter the minimum number of information gain to be expected from a parent node to its
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.
For further information about how the information gain is calculated, see Impurity and Information gain from the Spark documentation.
Minimum number of instances per
Enter the minimum number of training instances a node should have to make it valid for
The default value is 1, which means when a node has
Enter the maximum amount of memory (in MB) to be allocated to the training of the
Enter a number to indicate the checkpoint frequency. Every time at the end of the
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
The parameters you need to set are free parameters and so their values may be provided by
Therefore, you need to train the classifier model you are generating with different sets
You need to select the scores to be used depending on the algorithm you want to use to
For examples about how the confusion matrix is used in a
For a general explanation about confusion matrix, see https://en.wikipedia.org/wiki/Confusion_matrix from Wikipedia.
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.
No scenario is available for the Spark Batch version of this component