A model fitted on the training samples, can be validated on a separate validating set. The recall, precision, and accuracy of the model are computed.

1 | ```
doctor.validate(true.labels, predictions)
``` |

`true.labels` |
A vector of 0 and 1. |

`predictions` |
A vector of 0 and 1. |

F-measure is equal to: 2 times precision times recall / (precision+recall).

F-measure, precision, and recall are calculated. Also, the mis-labelled cases are reported.

Habil Zare

"Statistical Analysis of Overfitting Features", manuscript in preparation.

`FeaLect`

, `train.doctor`

, `doctor.validate`

,
`random.subset`

, `compute.balanced`

,`compute.logistic.score`

,
`ignore.redundant`

, `input.check.FeaLect`

1 2 3 4 5 | ```
tls <- c(1,1,1,0,0)
ps <- c(1,1,0,1,0)
names(tls) <- 1:5; names(ps) <- 1:5
doctor.validate(true.labels=tls, predictions=ps)
``` |

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