hist_binning_CV {CalibratR} | R Documentation |

trains and evaluates the histogram binning calibration model repeated `folds`

-Cross-Validation (CV).
The `predicted`

values are partitioned into n subsets. A histogram binning model is constructed on (n-1) subsets; the remaining set is used
for testing the model. All test set predictions are merged and used to compute error metrics for the model.

```
hist_binning_CV(actual, predicted, n_bins = 15, n_folds = 10, seed, input)
```

`actual` |
vector of observed class labels (0/1) |

`predicted` |
vector of uncalibrated predictions |

`n_bins` |
number of bins used in the histogram binning scheme, Default: 15 |

`n_folds` |
number of folds in the cross-validation, Default: 10 |

`seed` |
random seed to alternate the split of data set partitions |

`input` |
specify if the input was scaled or transformed, scaled=1, transformed=2 |

list object containing the following components:

`error` |
list object that summarizes discrimination and calibration errors obtained during the CV |

`type` |
"hist" |

`probs_CV` |
vector of calibrated predictions that was used during the CV |

`actual_CV` |
respective vector of true values (0 or 1) that was used during the CV |

[Package *CalibratR* version 0.1.2 Index]