# AUC measures

### Description

Set of functions to compute the Area Under the ROC Curve (AUC)

### Usage

1 2 3 | ```
AUC.single(pred, labels)
AUC.single.over.classes(target, predicted, g, root = "00")
compute.mean.AUC.single.over.classes(y)
``` |

### Arguments

`pred` |
numeric vector (scores) of the values of the predicted labels |

`labels` |
numeric vector of the true labels (0 negative, 1 positive examples) |

`target` |
matrix with the target multilabels: rows correspond to examples and columns to classes. target[i,j] = 1 if example i belongs to class j, target[i,j] = 0 otherwise. |

`predicted` |
a numeric matrix with predicted values (scores): rows correspond to examples and columns to classes. |

`g` |
a graph of class |

`root` |
the name of the root node (def. "00") |

`y` |
a list of lists. The components of the outer list is a list returned from the function |

### Details

`AUC.single`

computes the AUC for a single class.

`AUC.single.over.classes`

computes AUC for a set of classes, including their average values across classes and
the average values across the levels of the hierarchy (if any); level 1 classes are at distance 1 from the root,
level 2 the second level, till to last level correponding to the leaves. Note that if the argument g is missing no per-level values are computed.

`compute.mean.AUC.single.over.classes`

compute means across folds of AUC.single.over.classes. It can be used to automatically computed average values (for each class, level, or average across classes) across folds.

### Value

`AUC.single`

returns a numeric value corresponding to the AUC.

`AUC.single.over.classes`

returns a list with three elements:

`- average ` |
the average AUC across classes |

`- per.level` |
a named vector with average AUC for each level of the hierarchy; names correspond to levels |

`- per.class ` |
a named vector with AUC for each class; names correspond to classes |

`compute.mean.AUC.single.over.classes`

returns a list obtained by averaging the results across folds of the input y.
The components are:

`- average ` |
the average AUC across classes |

`- per.level` |
a named vector with average AUC for each level of the hierarchy; names correspond to levels |

`- per.class ` |
a named vector with AUC for each class; names correspond to classes |

### See Also

`F.measures`

, `PXR`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# preparing pseudo.random scores and target-labels for examples: 100 examples
# and 10 classes
Scores <- matrix(runif(1000),nrow=100);
Targets <- matrix(integer(1000),nrow=100);
Targets[Scores>0.5] <- 1;
# adding noise to scores
Scores <- Scores + matrix(rnorm(1000, sd=0.3),nrow=100);
colnames(Scores) <-colnames(Targets) <- LETTERS[1:10];
# getting scores and labels of class "A"
scores <- Scores[,"A"];
labels <- Targets[,"A"];
# AUC for a single class
AUC.single(scores,labels);
# AUC for the 10 classes
AUC.single.over.classes(Targets, Scores);
``` |

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker. Vote for new features on Trello.