How to interpret a ROC plot?

csoarescsoares Member Posts: 13 Contributor II
edited November 2019 in Help

I generate a ROC plot with the process given below and I get a ROC plot. I assume that the red line (ROC) is the proportion of TP against the proportion of FP but I can't understand what the blue line (ROC (Thresholds)) represents. Can anyone explain?


<operator name="Root" class="Process" expanded="yes">
    <operator name="ExcelExampleSource" class="ExcelExampleSource">
        <parameter key="excel_file" value="/Users/csoares/Documents/Ensino/DBM/Materiais/Catalog_multi_aula.xls"/>
        <parameter key="sheet_number" value="4"/>
        <parameter key="first_row_as_names" value="true"/>
        <parameter key="create_label" value="true"/>
        <parameter key="label_column" value="2"/>
        <parameter key="create_id" value="true"/>
    <operator name="SimpleValidation" class="SimpleValidation" expanded="yes">
        <parameter key="keep_example_set" value="true"/>
        <parameter key="create_complete_model" value="true"/>
        <operator name="NaiveBayes" class="NaiveBayes">
            <parameter key="keep_example_set" value="true"/>
        <operator name="OperatorChain" class="OperatorChain" expanded="yes">
            <operator name="ModelApplier" class="ModelApplier">
                <parameter key="keep_model" value="true"/>
                <list key="application_parameters">
            <operator name="ClassificationPerformance" class="ClassificationPerformance">
                <parameter key="keep_example_set" value="true"/>
                <parameter key="accuracy" value="true"/>
                <list key="class_weights">
            <operator name="ROCChart" class="ROCChart">
                <parameter key="use_model" value="false"/>


  • michaelglovenmichaelgloven RapidMiner Certified Analyst, Member Posts: 46 Guru

    I have the same question, I'm sure its a simple answer but can't find an explanation in the documentation.



  • sgenzersgenzer Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,959 Community Manager

    hello @michaelgloven - so for these kind of fundamental data science background topics I usually go "old school" with books (yes paper).  My go-to texts are "Data Mining for the Masses" by Dr. Matthew North, and "Predictive Analytics and Data Mining" by Kotu & Deshpande.  Both are excellent and are full of explicit examples using RapidMiner.  For your question about ROC curves, Chapter 8 of Kotu & Deshpande is all about Model Evaluation which starts with a long explanation of ROC.




  • michaelglovenmichaelgloven RapidMiner Certified Analyst, Member Posts: 46 Guru

    many thanks Scott. Figure 8.5 on page 269 of the Kotu book also has the ROC (thresholds) curve without explanation. It looks like the inverse of the ROC curve, probably a simple explanation, but still a mystery to me.  I'll check out your second resource suggestion.



  • tftemmetftemme Administrator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, RMResearcher, Member Posts: 164 RM Research

    Hi @michaelgloven,


    Each point of the ROC curve is the rate of true positives (or proportion of TP as it called in the first post) vs the rate of false positives (proportion of FP) for a specific applied threshold on the confidence of the corresponding classifier.


    The ROC (thresholds) curve just shows this confidence threshold (sometimes also called confidence cut).


    Hopes this helps,


    Best regards,

  • michaelglovenmichaelgloven RapidMiner Certified Analyst, Member Posts: 46 Guru

    Fabian, appreciate your explanation on the thresholds...I figured it was simple, but needed an expert to point it out!

  • PatricioWolffPatricioWolff Member Posts: 3 Contributor I
    In ROC(Threshold) curve the vertical axis indicates the threshold value and the horizontal axis shows the false positive rate.
  • SGolbertSGolbert RapidMiner Certified Analyst, Member Posts: 344 Unicorn
    edited September 2019
    Very roughly you have to look out for two things:
    1. The area under the curve (AUC): is the integral over the curve. Higher values translate to higher accuracy.
    2. The form of the curve: ideally the curve should be as smoother as possible. Large "jumps" indicate that the model is sensitive to small changes in the dataset. The initial jump is excepted.
Sign In or Register to comment.