Is it possible to predict two categories from one label?

Prentice · March 2019

I'm new to this software and was wondering if this is possible or if it only categorizes it to the closest category.

For example, if I have a raw line written like: "My brother likes oranges as well as bananas" and I have the categories Orange, Banana, Apple and Pear.
Is it possible to categorize this into Orange as well as Banana?

IngoRM · March 2019

Hi,

You could look at the confidence values and extract the top N predictions from those. Or you could simply use the operator "Generate Prediction Ranks" which is doing exactly that for a given number of desired classes. The process below shows a simple example.

Hope this helps,

Ingo

<?xml version="1.0" encoding="UTF-8"?><process version="9.2.000"><br>&nbsp; <context><br>&nbsp;&nbsp;&nbsp; <input/><br>&nbsp;&nbsp;&nbsp; <output/><br>&nbsp;&nbsp;&nbsp; <macros/><br>&nbsp; </context><br>&nbsp; <operator activated="true" class="process" compatibility="9.2.000" expanded="true" name="Process"><br>&nbsp;&nbsp;&nbsp; <parameter key="logverbosity" value="init"/><br>&nbsp;&nbsp;&nbsp; <parameter key="random_seed" value="2001"/><br>&nbsp;&nbsp;&nbsp; <parameter key="send_mail" value="never"/><br>&nbsp;&nbsp;&nbsp; <parameter key="notification_email" value=""/><br>&nbsp;&nbsp;&nbsp; <parameter key="process_duration_for_mail" value="30"/><br>&nbsp;&nbsp;&nbsp; <parameter key="encoding" value="UTF-8"/><br>&nbsp;&nbsp;&nbsp; <process expanded="true"><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <operator activated="true" class="retrieve" compatibility="9.2.000" expanded="true" height="68" name="Retrieve Titanic Training" width="90" x="45" y="34"><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="repository_entry" value="//Samples/data/Titanic Training"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </operator><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <operator activated="true" class="select_attributes" compatibility="9.2.000" expanded="true" height="82" name="Select Attributes" width="90" x="179" y="34"><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="attribute_filter_type" value="single"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="attribute" value="Survived"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="attributes" value=""/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="use_except_expression" value="false"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="value_type" value="attribute_value"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="use_value_type_exception" value="false"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="except_value_type" value="time"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="block_type" value="attribute_block"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="use_block_type_exception" value="false"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="except_block_type" value="value_matrix_row_start"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="invert_selection" value="true"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="include_special_attributes" value="true"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </operator><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <operator activated="true" class="set_role" compatibility="9.2.000" expanded="true" height="82" name="Set Role" width="90" x="313" y="34"><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="attribute_name" value="Passenger Class"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="target_role" value="label"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <list key="set_additional_roles"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </operator><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <operator activated="true" class="naive_bayes" compatibility="9.2.000" expanded="true" height="82" name="Naive Bayes" width="90" x="447" y="34"><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="laplace_correction" value="true"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </operator><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <operator activated="true" class="retrieve" compatibility="9.2.000" expanded="true" height="68" name="Retrieve Titanic Unlabeled" width="90" x="313" y="136"><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="repository_entry" value="//Samples/data/Titanic Unlabeled"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </operator><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <operator activated="true" class="set_role" compatibility="9.2.000" expanded="true" height="82" name="Set Role (2)" width="90" x="447" y="136"><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="attribute_name" value="Passenger Class"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="target_role" value="label"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <list key="set_additional_roles"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </operator><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <operator activated="true" class="apply_model" compatibility="9.2.000" expanded="true" height="82" name="Apply Model" width="90" x="581" y="34"><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <list key="application_parameters"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="create_view" value="false"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </operator><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <operator activated="true" class="generate_prediction_ranking" compatibility="9.2.000" expanded="true" height="82" name="Generate Prediction Ranking" width="90" x="715" y="34"><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="number_of_ranks" value="2"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <parameter key="remove_old_predictions" value="true"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </operator><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <connect from_op="Retrieve Titanic Training" from_port="output" to_op="Select Attributes" to_port="example set input"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <connect from_op="Select Attributes" from_port="example set output" to_op="Set Role" to_port="example set input"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <connect from_op="Set Role" from_port="example set output" to_op="Naive Bayes" to_port="training set"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <connect from_op="Naive Bayes" from_port="model" to_op="Apply Model" to_port="model"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <connect from_op="Retrieve Titanic Unlabeled" from_port="output" to_op="Set Role (2)" to_port="example set input"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <connect from_op="Set Role (2)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <connect from_op="Apply Model" from_port="labelled data" to_op="Generate Prediction Ranking" to_port="example set input"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <connect from_op="Generate Prediction Ranking" from_port="example set output" to_port="result 1"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <portSpacing port="source_input 1" spacing="0"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <portSpacing port="sink_result 1" spacing="0"/><br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <portSpacing port="sink_result 2" spacing="0"/><br>&nbsp;&nbsp;&nbsp; </process><br>&nbsp; </operator><br></process>

Prentice · March 2019

Thanks, I will try this once I get to it.

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Is it possible to predict two categories from one label?

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