tNaiveBayesModel properties for Apache Spark Batch

For Spark V1.4 onwards, the parameters to be set are:

• 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.

• 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.

Complete this table to define the feature type of each input column in order to compute the classifier model.

• Column: this column lists the input column automatically retrieved from the input schema.

• Usage: select the type of the feature that the records from each input column represent.

  For example, people's ages represent the continuous feature while people's genders the categorical feature (also called discrete feature).

  If you select Label for an input column, the records of this column are used as the class names (Target in terms of classification) of the elements to be classified; if you need to ignore a column in the model computation, select Unused.

• Bin edges: this column is activated only when the input column represents the continuous feature. It allows you to discretize the continuous data into bins, that is to say, to partition the continuous data into half-open segments by putting boundary values within double quotation marks.

  For example, if you enter "18;35" for a column as to people's ages, these ages will be grouped into three segments, among which the ages less or equal to 18 go into a segment, ages greater than 18 and less or equal to 35 into a segment and ages greater than 35 the other segment.

• Categories: this column is activated only when the input column represents the categorical feature. You need to enter the names of each category to be used and separate them using a semicolon (;), for example, "male;female".

  Note that the categories you enter must exist in the input column.

• Class name: this column is activated only when the Label option has been selected in the Usage column. You need to enter the name of the classes used in the classification and separate them using a semicolon (;), for example, "platinum-level customer;gold-level customer".

• The Accuracy score varies from 0 to 1 to indicate how accurate a classification is. More approximate to 1 an Accuracy score is, more accurate the corresponding classification is.

• The Precision score, also varying from 0 to 1, indicates how relevant the elements selected by the classification are to a given class.

• The Recall score, still varying from 0 to 1, indicates how many relevant elements are selected.

• The F1-measure score is the harmonic mean of the Precision score and the Recall score.

These scores can be output to the console of the Run view when you execute the Job when you have added the following code to the Log4j view in the Project Settings dialog box.

<!-- DataScience Logger -->
<logger name= "org.talend.datascience.mllib" additivity= "false" >
<level value= "INFO" />
<appender-ref ref= "CONSOLE" />
</logger>