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Data Mining Success Metrics
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Area Under the ROC Curve (AUC-ROC)
The Area Under the Receiver Operating Characteristic Curve measures the model's ability to distinguish between classes. An area of 1 represents a perfect model, while an area of 0.5 represents a worthless model.
F1-Score
The F1-Score is the harmonic mean of precision and recall, providing a balance between the two. It is useful when you need to take both false positives and false negatives into account.
Accuracy
Accuracy measures the proportion of true results, both true positives and true negatives, in your data mining model. It is the ratio of correctly predicted instances to the total instances.
Recall
Recall, or Sensitivity, measures the ratio of true positives identified compared to the total number of actual positives. It demonstrates the ability to find all relevant instances in a dataset.
Precision
Precision reflects the number of true positive results divided by the number of all positive results predicted by the classifier. It measures the relevancy of obtained results.
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