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Data Mining Success Metrics

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Area Under the ROC Curve (AUC-ROC)

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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.

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F1-Score

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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.

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Accuracy

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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.

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Recall

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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.

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Precision

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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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