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AUC (Area Under the Curve): Difference between revisions
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The choice of [[algorithm]] can significantly influence an AUC score. Some algorithms may be better suited for certain types of data or may perform better on small or large datasets, depending on its quality and quantity. Furthermore, training data quality and quantity also factor into calculating an AUC score since classifiers only learn patterns present in training data. | The choice of [[algorithm]] can significantly influence an AUC score. Some algorithms may be better suited for certain types of data or may perform better on small or large datasets, depending on its quality and quantity. Furthermore, training data quality and quantity also factor into calculating an AUC score since classifiers only learn patterns present in training data. | ||
The [[ | The [[feature]]s used to train the classifier can have an important influence on its AUC score. Selecting relevant features that are helpful for classification can improve the performance of the classifier. Furthermore, tuning [[hyperparameters]] of a model may influence its AUC score; selecting suitable values will improve performance overall. | ||
==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||