Description Usage Arguments Details Value Author(s) See Also Examples

modelingSummary is an automatic function for modeling data, it returns a dataframe containing the metrics of the modeling using five machine learning algorithms: KNN, SVM, RF, NNET, and Bcart. This function is based on spliData, tuneTrain, predict, and getMetrics functions.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |

`data` |
object of class "data.frame" with target variable and predictor variables. |

`y` |
character. Target variable. |

`p` |
numeric. Proportion of data to be used for training. Default: 0.7 |

`length` |
integer. Number of values to output for each tuning parameter. If |

`control` |
character. Resampling method to use. Choices include: "boot", "boot632", "optimism_boot", "boot_all", "cv", "repeatedcv", "LOOCV", "LGOCV", "none", "oob", timeslice, "adaptive_cv", "adaptive_boot", or "adaptive_LGOCV". Default: "repeatedcv". See |

`number` |
integer. Number of cross-validation folds or number of resampling iterations. Default: 10. |

`repeats` |
integer. Number of folds for repeated k-fold cross-validation if "repeatedcv" is chosen as the resampling method in |

`process` |
character. Defines the pre-processing transformation of predictor variables to be done. Options are: "BoxCox", "YeoJohnson", "expoTrans", "center", "scale", "range", "knnImpute", "bagImpute", "medianImpute", "pca", "ica", or "spatialSign". See |

`summary` |
expression. Computes performance metrics across resamples. For numeric |

`positive` |
character. The positive class for the target variable if |

`parallelComputing` |
logical. indicates whether to also use the parallel processing. Default: False |

`classtype` |
integer.indicates the number of classes of the traits. |

`...` |
additional arguments to be passed to |

Types of classification and regression models available for use with `tuneTrain`

can be found using `names(getModelInfo())`

. The results given depend on the type of model used.

A dataframe contains the metrics of the modeling of five machine learning algorithms: KNN, SVM, RF, NNET, and Bcart.

`tuneTrain`

relies on package `caret`

to perform the modeling.

Zakaria Kehel, Khadija Aziz

`createDataPartition`

,
`trainControl`

,
`train`

,
`predict.train`

,
`confusionMatrix`

1 2 3 4 | ```
if(interactive()){
data(septoriaDurumWC)
models <- modelingSummary(data = septoriaDurumWC, y = "ST_S", positive = "R", classtype = 2)
}
``` |

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