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"Interpretation/Meaning of Performance Measure"

choose_usernamechoose_username Member Posts: 33  Maven
edited June 2019 in Help
Hello,

i have build a workflow, which shall classify examples with a decision tree. I used a Performance-measure-Operator too. In the result are accuracy , precision and recall listed.

What does this measures mean? Is there a difference between accuracy and precision and what is the meaning of recall?

greetings

user
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Answers

  • IngoRMIngoRM Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, Community Manager, RMResearcher, Member, University Professor Posts: 1,751  RM Founder
    Hi,

    those measures are all taken from a confusion matrix (http://en.wikipedia.org/wiki/Confusion_matrix):
    Actually PositiveActually Negative
    Predicted Positiveab
    Predicted Negativecd
    After the prediction, RapidMiner checks for each example the true label and the prediction and sorts the example into the correct place in the confusion matrix, i.e. increases the count by 1. The sum of a,b,c, and d hence is the total number of examples. The value "a" is called "true positives", "b" is called "false positives", "c" is called "false negatives", and "d" is called "true negatives".

    The ratio of correctly classified examples compared to the number of all examples is called accuracy and is calculated as (a+d)/(a+b+c+d).

    The ratio of true positives to all as positive predicted examples is called precision and is calculated as a / (a+b).

    The ratio of true positives to all actually positive examples is called recall and is calculated as a / (a+c).

    Accuracy is also available in the case of more than two classes, precision and recall are only available for two-class-problems (you can, however, always calculate a per class precision and recall).

    Cheers,
    Ingo


  • choose_usernamechoose_username Member Posts: 33  Maven
    thx for the answer. i conclude from this, that the most interessting measure (overall) is the accuracy and the other ones are more class specific.



    Greetings

    Nutzer
  • IngoRMIngoRM Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, Community Manager, RMResearcher, Member, University Professor Posts: 1,751  RM Founder
    Hello,

    i conclude from this, that the most interessting measure (overall) is the accuracy and the other ones are more class specific.
    well, it depends. If you know nothing about your classes and the costs for different types of errors, accuracy might indeed a good value to optimize. In other cases, where the application requires a high precision or recall for a specific class, you should definitely go for them. Or you introduce costs for different errors. Or...

    If you have more than two classes and search for a single-number-evaluation to optimize there is not much left beside accuracy and kappa (and some others).

    Cheers,
    Ingo
  • choose_usernamechoose_username Member Posts: 33  Maven
    Hi,


    thanks for the information. I will keep that in mind, when working on it.


    greetings

    User
  • wesselwessel Member Posts: 537  Guru
    Ingo Mierswa wrote:

    If you have more than two classes and search for a single-number-evaluation to optimize there is not much left beside accuracy and kappa (and some others).
    How is the kappa score calculated in Rapid Miner?

    I always think of it as the normalized accuracy score.
    Kappa = 0, all predictions are the majority class.
    Kappa = 1, all predictions are correct.
    Kappa = -1, all predictions are wrong.

    If you know only know accuracy is 99%, you don't really know much.
    Because you might have a dataset with 9900 negative and only 100 positive example.
    And then you are only interested in systems with an accuracy greater then 99%.


  • IngoRMIngoRM Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, Community Manager, RMResearcher, Member, University Professor Posts: 1,751  RM Founder
    Hi,

    here it is (fresh from the source codeΒ  ;) ):

    double pa = accuracy;
    double pe = 0.0d;
    for (int i = 0; i < counter.length; i++) {
    double row = 0.0d;
    double column = 0.0d;
    for (int j = 0; j < counter.length; j++) {
    row += counter;
    column += counter;
    }
    //pe += ((row * column) / Math.pow(total, counter.length));
    pe += ((row * column) / (total * total));
    }
    return (pa - pe) / (1.0d - pe);
    Cheers,
    Ingo
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