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"Apply Threshold" operator isn't reading data from my "confidence" attributes
I have two attributes: "Data" with binomial values "Y" and "N", and "Predicted" with real values between 0 and 1. I want to apply a threshold to the "Predicted" attribute to convert it into binomial values, then test its accuracy against the "Data" attribute.
to use the operator "Apply Threshold", I had to make two real confidence attributes, "confidence(Y)" and "confidence(N)" (values set to "Predicted" and 1  "Predicted") with roles by the same names, and a binomial "pred" attribute with role "prediction" (values initially copied from "Data").
however, when I run the process, it replaces each value of "pred" to "N", regardless of whether "confidence(positive)" was greater than the threshold. that suggests to me that "Apply Threshold" is failing to read the "confidence(positive)" attribute, but I don't know why or how to fix it.
to use the operator "Apply Threshold", I had to make two real confidence attributes, "confidence(Y)" and "confidence(N)" (values set to "Predicted" and 1  "Predicted") with roles by the same names, and a binomial "pred" attribute with role "prediction" (values initially copied from "Data").
however, when I run the process, it replaces each value of "pred" to "N", regardless of whether "confidence(positive)" was greater than the threshold. that suggests to me that "Apply Threshold" is failing to read the "confidence(positive)" attribute, but I don't know why or how to fix it.
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key="generator_type" value="comma separated text"/> <parameter key="number_of_examples" value="100"/> <parameter key="use_stepsize" value="false"/> <list key="function_descriptions"/> <parameter key="add_id_attribute" value="false"/> <list key="numeric_series_configuration"/> <list key="date_series_configuration"/> <list key="date_series_configuration (interval)"/> <parameter key="date_format" value="yyyyMMdd HH:mm:ss"/> <parameter key="time_zone" value="SYSTEM"/> <parameter key="input_csv_text" value="Data,Predicted (Model) Y,0.225135637 N,0.015354609 N,0.0001 Y,0.417204888 N,0.0001 Y,0.825845697 Y,0.566671094 N,0.361344126 Y,0.138533613 N,0.0001 N,0.0001 N,0.10015621 N,0.519371562 Y,0.687709876 Y,0.488898237 Y,0.471409091 N,0.118468641 Y,0.373571663 N,0.0001 N,0.078786849 N,0.067795272 Y,0.421387476 Y,0.83791151 N,0.0001 Y,0.457122298 N,0.405682588 Y,0.580764825 Y,0.218196512 Y,0.655224335 Y,0.855784984 Y,0.50579238 Y,0.084138364 N,0.0001 Y,0.821532224 Y,0.678781002 Y,0.926816559 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N,0.0001 N,0.0001 N,0.034608624 Y,0.411143627 Y,0.403309021 Y,0.690479379 Y,0.58645295 Y,0.703471435 N,0.240645419 N,0.317348965 N,0.0001 N,0.0001 Y,0.479985015 N,0.051663453 N,0.151098679 Y,0.400885126 Y,0.669478979 N,0.0001 N,0.084509219 N,0.104969007 N,0.202747595 N,0.0001 Y,0.248550676 Y,0.736360718 Y,0.507362049 Y,0.45185447 N,0.170658129 N,0.0001 Y,0.492524913 Y,0.863828983 Y,0.505563511 Y,0.855626349 Y,0.214503677 Y,0.667043014 N,0.04338791 N,0.268321785 N,0.163068649 N,0.032860214 Y,0.432603468 N,0.050831568 Y,0.282183637 Y,0.497255402 Y,0.507714476 N,0.048046271 N,0.302599739 Y,0.552843342 N,0.0001 N,0.0001 N,0.045763161 N,0.0001 Y,0.35923354 Y,0.275079272 Y,0.490030294 N,0.093214431 Y,0.33982672 Y,0.228286029 N,0.173078966 N,0.24842934 N,0.0001 Y,0.215257771 N,0.10263881 Y,0.609961692 Y,0.153339016 N,0.040604182 N,0.0001 Y,0.420378131 N,0.035050649 N,0.0001 Y,0.956775757 N,0.312246201 N,0.0001 Y,0.619621516 N,0.062474506 Y,0.83682199 N,0.0001 N,0.0001 N,0.087984095 Y,0.513109824 Y,0.168519626 N,0.0001 N,0.0125382 Y,0.623915238 N,0.299362197 Y,0.700795017 Y,0.639515816 N,0.0001 Y,0.423247208 Y,0.314171344 N,0.0001 Y,0.675042884 Y,0.858816065 N,0.0001 N,0.262590264 N,0.107755031 Y,0.642963514 Y,0.684925359 Y,0.439486907 Y,0.474113532 Y,0.248236629 Y,0.573986919 N,0.015263223 Y,0.65455098 Y,0.699771428"/> <parameter key="column_separator" value=","/> <parameter key="parse_all_as_nominal" value="false"/> <parameter key="decimal_point_character" value="."/> <parameter key="trim_attribute_names" value="true"/> </operator> <operator activated="true" class="generate_attributes" compatibility="9.6.000" expanded="true" height="82" name="Generate Attributes" width="90" x="179" y="238"> <list key="function_descriptions"> <parameter key="pred" value="Data"/> <parameter key="confidence(N)" value="1[Predicted (Model)]"/> <parameter key="confidence(Y)" value="[Predicted (Model)]"/> </list> <parameter key="keep_all" value="true"/> </operator> <operator activated="true" class="nominal_to_binominal" compatibility="9.6.000" expanded="true" height="103" name="Nominal to Binominal" width="90" x="313" y="238"> <parameter key="return_preprocessing_model" value="false"/> <parameter key="create_view" value="false"/> <parameter key="attribute_filter_type" value="single"/> <parameter key="attribute" value="pred"/> <parameter key="attributes" value=""/> <parameter key="use_except_expression" value="false"/> <parameter key="value_type" value="nominal"/> <parameter key="use_value_type_exception" value="false"/> <parameter key="except_value_type" value="file_path"/> <parameter key="block_type" value="single_value"/> <parameter key="use_block_type_exception" value="false"/> <parameter key="except_block_type" value="single_value"/> <parameter key="invert_selection" value="false"/> <parameter key="include_special_attributes" value="false"/> <parameter key="transform_binominal" value="false"/> <parameter key="use_underscore_in_name" value="false"/> </operator> <operator activated="true" class="set_role" compatibility="9.6.000" expanded="true" height="82" name="Set Role" width="90" x="447" y="238"> <parameter key="attribute_name" value="Data"/> <parameter key="target_role" value="label"/> <list key="set_additional_roles"> <parameter key="pred" value="prediction"/> <parameter key="confidence(N)" value="confidence(N)"/> <parameter key="confidence(Y)" value="confidence(Y)"/> </list> </operator> <operator activated="true" class="select_attributes" compatibility="9.6.000" expanded="true" height="82" name="Select Attributes" width="90" x="581" y="238"> <parameter key="attribute_filter_type" value="single"/> <parameter key="attribute" value="Predicted (Model)"/> <parameter key="attributes" value=""/> <parameter key="use_except_expression" value="false"/> <parameter key="value_type" value="attribute_value"/> <parameter key="use_value_type_exception" value="false"/> <parameter key="except_value_type" value="time"/> <parameter key="block_type" value="attribute_block"/> <parameter key="use_block_type_exception" value="false"/> <parameter key="except_block_type" value="value_matrix_row_start"/> <parameter key="invert_selection" value="true"/> <parameter key="include_special_attributes" value="false"/> </operator> <operator activated="true" breakpoints="before" class="apply_threshold" compatibility="9.6.000" expanded="true" height="82" name="Apply Threshold" width="90" x="715" y="238"/> <connect from_op="Create Threshold" from_port="output" to_op="Apply Threshold" to_port="threshold"/> <connect from_op="Create ExampleSet" from_port="output" to_op="Generate Attributes" to_port="example set input"/> <connect from_op="Generate Attributes" from_port="example set output" to_op="Nominal to Binominal" to_port="example set input"/> <connect from_op="Nominal to Binominal" from_port="example set output" to_op="Set Role" to_port="example set input"/> <connect from_op="Set Role" from_port="example set output" to_op="Select Attributes" to_port="example set input"/> <connect from_op="Select Attributes" from_port="example set output" to_op="Apply Threshold" to_port="example set"/> <connect from_op="Apply Threshold" from_port="example set" to_port="result 1"/> <portSpacing port="source_input 1" spacing="0"/> <portSpacing port="sink_result 1" spacing="0"/> <portSpacing port="sink_result 2" spacing="0"/> </process> </operator> </process>
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Best Answer

OptionsBalazsBarany Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert Posts: 955 UnicornHi @tors,
I looked at a different process and how it creates the confidence attributes. The role is named slightly differently, like this:
confidence_value.
So changing your process to assign the roles confidence_N and confidence_Y solves the problem. I was able to enter different thresholds in Create Threshold and got the expected result.
Regards,
Balázs3
Answers
if you name your attributes and roles correctly, it will work.
The confidences have to be confidence(N) and confidence(Y) in this situation, as your prediction contains Y or N.
It was hard to reproduce your process without the incoming data. For test data in the Community, it's easy to use Create ExampleSet with generator type = comma separated text and just enter some example data.
Regards,
Balázs