Type Alias ModelMetricsBinary

Binary metrics for categorical values with two options. Details: https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/AutoML/scoring-binary-classification.htm

type ModelMetricsBinary = {
    accuracy?: number;
    accuracyTest?: number;
    auc?: number;
    aucTest?: number;
    f1?: number;
    f1Test?: number;
    fallout?: number;
    falloutTest?: number;
    falseNegative?: number;
    falseNegativeTest?: number;
    falsePositive?: number;
    falsePositiveTest?: number;
    logLoss?: number;
    logLossTest?: number;
    mcc?: number;
    mccTest?: number;
    missRate?: number;
    missRateTest?: number;
    npv?: number;
    npvTest?: number;
    precision?: number;
    precisionTest?: number;
    recall?: number;
    recallTest?: number;
    specificity?: number;
    specificityTest?: number;
    threshold?: number;
    thresholdTest?: number;
    trueNegative?: number;
    trueNegativeTest?: number;
    truePositive?: number;
    truePositiveTest?: number;
}
Index

Properties

accuracy? accuracyTest? auc? aucTest? f1? f1Test? fallout? falloutTest? falseNegative? falseNegativeTest? falsePositive? falsePositiveTest? logLoss? logLossTest? mcc? mccTest? missRate? missRateTest? npv? npvTest? precision? precisionTest? recall? recallTest? specificity? specificityTest? threshold? thresholdTest? trueNegative? trueNegativeTest? truePositive? truePositiveTest?

Properties

accuracy?: number

Average measure of how often the model made correct predictions (training data)

accuracyTest?: number

Average measure of how often the model made correct predictions (test data)

auc?: number

Area under curve (training data)

aucTest?: number

Area under curve (test data)

f1?: number

The harmonic mean of precision and recall for training data

f1Test?: number

The harmonic mean of precision and recall for test data

fallout?: number

The false positive rate (training data)

falloutTest?: number

The false positive rate (test data)

falseNegative?: number

Actual true values incorrectly predicted as false (training data)

falseNegativeTest?: number

Actual true values incorrectly predicted as false (test data)

falsePositive?: number

Actual false values incorrectly predicted as true (training data)

falsePositiveTest?: number

Actual false values incorrectly predicted as true (test data)

logLoss?: number

Measures accuracy in logistic regression (training data)

logLossTest?: number

Measures accuracy in logistic regression (test data)

mcc?: number

Matthews correlation coefficient (training data)

mccTest?: number

Matthews correlation coefficient (test data)

missRate?: number

The false negative rate (training data)

missRateTest?: number

The false negative rate (test data)

npv?: number

Negative predictive value (training data)

npvTest?: number

Negative predictive value (test data)

precision?: number

Positive predictive value. Probability that the model was correct when it predicted something was true. (training data)

precisionTest?: number

Positive predictive value. Probability that the model was correct when it predicted something was true. (test data)

recall?: number

The true positive rate (training data)

recallTest?: number

The true positive rate (test data)

specificity?: number

The true negative rate (training data)

specificityTest?: number

The true negative rate (test data)

threshold?: number

Probability that a prediction is true (training data)

thresholdTest?: number

Probability that a prediction is true (test data)

trueNegative?: number

Actual false values correctly predicted as false (training data)

trueNegativeTest?: number

Actual false values correctly predicted as false (test data)

truePositive?: number

Actual true values correctly predicted as true (training data)

truePositiveTest?: number

Actual true values correctly predicted as true (test data)

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