AUC (Area Under the Curve): Difference between revisions

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{{see also|machine learning terms}}
==Introduction==
==Introduction==
In machine learning, the AUC (Area Under the ROC Curve) is a popular metric used to evaluate the performance of binary classification models. It measures the ability of the model to distinguish between the positive and negative classes based on the output probabilities of the model.
In [[machine learning]], the '''Area Under the ROC Curve (AUC)''' is a popular [[metric]] to assess the performance of [[binary classification]] [[models]]. This measure assesses its ability to discriminate between positive and negative [[class]]es based on [[output]] probabilities from the model.


==What is AUC?==
==What is AUC?==
AUC is a measure of the area under the curve of a Receiver Operating Characteristic (ROC) curve, which is a graph that represents the trade-off between the true positive rate (TPR) and the false positive rate (FPR) of a binary classifier. The ROC curve plots the TPR on the y-axis against the FPR on the x-axis at various probability thresholds.
AUC is a measure of the area under the curve on a [[Receiver Operating Characteristic (ROC) curve]], which illustrates the trade-off between [[true positive rate]] (TPR) and [[false positive rate]] (FPR) for binary classifiers. The ROC graph plots TPR on one y-axis against FPR on another at various probability thresholds.


The AUC score ranges between 0 and 1, where 0.5 is the score of a random classifier, and 1.0 is the score of a perfect classifier. A higher AUC score indicates that the classifier has a better ability to distinguish between the positive and negative classes.
The AUC score for a classifier ranges between 0 and 1, with 0.5 being the score for a random classifier and 1.0 representing perfection. A higher AUC indicates that the classifier has improved at distinguishing between positive and negative classes.


The AUC score provides a summary of the classifier's performance across all possible probability thresholds. This means that it is not affected by the specific threshold used for classification, which can vary depending on the application.
The AUC score provides a snapshot of the classifier's performance across all potential probability thresholds, meaning it is unaffected by the specific threshold used for [[classification]], which may vary depending on the application.


==Why is AUC Used?==
==Why Is AUC Used?==
AUC is used to evaluate the performance of binary classifiers when the classes in the dataset are imbalanced, meaning that one class has significantly more samples than the other. In such cases, the accuracy of the classifier can be misleading since a classifier can achieve a high accuracy by simply predicting the majority class.
AUC is used to assess the performance of binary classifiers when their classes have significantly more samples than another. In such cases, [[accuracy]] may not reflect true [[precision]] since a classifier may achieve high accuracy by simply correctly predicting which [[majority class]] will pass inspection.


AUC provides a more comprehensive evaluation of the classifier's ability to correctly classify the positive and negative classes, regardless of the class distribution. It is a widely used metric in various applications, such as credit scoring, medical diagnosis, and fraud detection.
AUC provides a more thorough assessment of a classifier's ability to correctly classify positive and negative classes, regardless of class distribution. It has become widely used in various applications such as credit scoring, medical diagnosis, and fraud detection.


==How is AUC Calculated?==
==How is AUC Calculated?==
The AUC score is calculated by integrating the ROC curve. The ROC curve is created by plotting the TPR against the FPR at various probability thresholds. The integration of the ROC curve provides the AUC score.
The AUC score is calculated by integrating the ROC curve. This plots TPR against FPR at various probability thresholds, and then integrating that curve gives us our AUC score.


The integration of the ROC curve can be approximated using numerical methods such as the trapezoidal rule, Simpson's rule, or the Riemann sum. These numerical methods provide an approximation of the area under the curve.
To approximate the integration of an ROC curve, numerical methods such as trapezoidal rule, Simpson's rule or Riemann sum can be employed. These numerical formulas provide a close approximation to the area under the curve.


==Factors Affecting AUC==
==Factors Affecting AUC==
The AUC score of a classifier can be affected by various factors, such as the choice of algorithm, the quality and quantity of training data, the choice of features, and the hyperparameters used for model tuning.
The accuracy of an algorithm's AUC score can vary based on several factors, including the quality and quantity of [[training data]], [[feature selection]], and [[hyperparameter tuning]] used for model tuning.


The choice of algorithm can significantly affect the AUC score. Some algorithms may be better suited for certain types of data or may perform better on small or large datasets. The quality and quantity of training data can also affect the AUC score since a classifier can only learn patterns that are present in the 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 choice of features used to train the classifier can also have a significant impact on the AUC score. Choosing relevant features that are informative for the classification task can improve the performance of the classifier. Finally, the hyperparameters used to tune the model can affect the AUC score, and choosing the right hyperparameters can improve the performance of the model.
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)==
AUC is like a score that tells us how good a robot is at telling things apart. For example, if the robot is trained to tell the difference between cats and dogs, it gets a score based on how many cats it can find out of all the things it looks at. The higher the score, the better the robot is at telling cats and dogs apart.
AUC is like a score that tells us how well a robot is at discriminating things apart. For instance, if it has been trained to distinguish between cats and dogs, its score would be based on how many cats it can identify from all other items it examines. The higher this number is, the better equipped the robot becomes at telling cats from dogs."
 
[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]

Latest revision as of 21:21, 17 March 2023

See also: machine learning terms

Introduction

In machine learning, the Area Under the ROC Curve (AUC) is a popular metric to assess the performance of binary classification models. This measure assesses its ability to discriminate between positive and negative classes based on output probabilities from the model.

What is AUC?

AUC is a measure of the area under the curve on a Receiver Operating Characteristic (ROC) curve, which illustrates the trade-off between true positive rate (TPR) and false positive rate (FPR) for binary classifiers. The ROC graph plots TPR on one y-axis against FPR on another at various probability thresholds.

The AUC score for a classifier ranges between 0 and 1, with 0.5 being the score for a random classifier and 1.0 representing perfection. A higher AUC indicates that the classifier has improved at distinguishing between positive and negative classes.

The AUC score provides a snapshot of the classifier's performance across all potential probability thresholds, meaning it is unaffected by the specific threshold used for classification, which may vary depending on the application.

Why Is AUC Used?

AUC is used to assess the performance of binary classifiers when their classes have significantly more samples than another. In such cases, accuracy may not reflect true precision since a classifier may achieve high accuracy by simply correctly predicting which majority class will pass inspection.

AUC provides a more thorough assessment of a classifier's ability to correctly classify positive and negative classes, regardless of class distribution. It has become widely used in various applications such as credit scoring, medical diagnosis, and fraud detection.

How is AUC Calculated?

The AUC score is calculated by integrating the ROC curve. This plots TPR against FPR at various probability thresholds, and then integrating that curve gives us our AUC score.

To approximate the integration of an ROC curve, numerical methods such as trapezoidal rule, Simpson's rule or Riemann sum can be employed. These numerical formulas provide a close approximation to the area under the curve.

Factors Affecting AUC

The accuracy of an algorithm's AUC score can vary based on several factors, including the quality and quantity of training data, feature selection, and hyperparameter tuning used for model tuning.

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 features 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)

AUC is like a score that tells us how well a robot is at discriminating things apart. For instance, if it has been trained to distinguish between cats and dogs, its score would be based on how many cats it can identify from all other items it examines. The higher this number is, the better equipped the robot becomes at telling cats from dogs."