A method of data classification for use in a wireless communication system includes obtaining decoder metrics from a decoder. The decoder metrics correspond to data generated by the decoder. The decoder metrics
fairnessMetrics computes fairness metrics (bias and group metrics) for a data set or binary classification model with respect to sensitive attributes.
Lüth, Lukas Klein, and Till J. Bungert. A call to reflect on evaluation practices for failure detection in image classification. Preprint at arXiv https://doi.org/10.48550/arXiv.2211.15259 (2023). Université de Montréal. The Declaration - Montreal Responsible AI, 2017. https://declaration...
Given streaming data, updateMetrics measures the performance of a configured multiclass error-correcting output codes (ECOC) classification model for incremental learning (incrementalClassificationECOC object).
Version Control Metric (8):The authors of Paper P68 use metrics such as ‘Commit Count’ (M71), ‘Star Count’ (M260), and ‘Fork Count’ (M130) for subject selection of repositories forvulnerability analysis. Data Flow Metrics (2):We collected measurements of data flow in the Data Flow...
TestCaseResultIdAndRev 函數 TestCaseResultIdentifier 測試 TestCaseResultsData 測試 TestCaseResultUpdateModel TestConfiguration 測試 TestConfiguration 測試 TestConfigurationCreateUpdateParameters TestConfiguration引用 TestConfigurationState 測試 TestEntityCount 測試 TestEntityTypes 測試環境 TestExecutionReportData (測試執...
Given streaming data, updateMetricsAndFit first evaluates the performance of a configured incremental learning model for linear regression (incrementalRegressionLinear object) or linear binary classification (incrementalClassificationLinear object) by calling updateMetrics on incoming data. Then updateMetricsAnd...
The class and subclass provide a two-level classification. The high byte of this field contains the family class, while the low byte contains the family subclass. The interpretation of subclass values depends on the class value. Registered class and subclass values were originally defined by IBM....
True negative rate (TNR): the ratio of negative instances that are correctly classified as negative TNR = TN/(TN+FP) = specify False positive rate (FPR): the ratio of negative instances that are incorrectly classified as positive. FPR = FN/(TN+FP) = 1-specify ...
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