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

This generic function tunes hyperparameters of statistical methods using a grid search over supplied parameter ranges.

1 2 3 4 5 6 |

`train.x` |
either a formula or a ' |

`train.y` |
the response variable if |

`weight` |
the weight of each subject. It should be in the same length of |

`use_zero_weight` |
if |

`pre.check` |
if |

`data` |
data, if a formula interface is used. Ignored, if predictor matrix and response are supplied directly. |

`validation.x` |
an optional validation set. Depending on whether a
formula interface is used or not, the response can be
included in |

`validation.y` |
if no formula interface is used, the response of
the (optional) validation set. Only used for bootstrap and fixed validation
set (see |

`validation.weight` |
the weight of each subject in the validation set. Will be set to 1, if the user does not provide. |

`weigthed.error` |
if |

`ranges` |
a named list of parameter vectors spanning the sampling
space. See |

`predict.func` |
optional predict function, if the standard |

`tunecontrol` |
object of class |

`...` |
Further parameters passed to the training functions. |

As performance measure, the classification error is used for classification, and the mean squared error for regression. It is possible to specify only one parameter combination (i.e., vectors of length 1) to obtain an error estimation of the specified type (bootstrap, cross-classification, etc.) on the given data set.

Cross-validation randomizes the data set before building the splits
whichâ€”once createdâ€”remain constant during the training
process. The splits can be recovered through the `train.ind`

component of the returned object.

For `tune_wsvm`

, an object of class `tune_wsvm`

, including the components:

`best.parameters` |
a 1 x k data frame, k number of parameters. |

`best.performance` |
best achieved performance. |

`performances` |
if requested, a data frame of all parameter combinations along with the corresponding performance results. |

`train.ind` |
list of index vectors used for splits into training and validation sets. |

`best.model` |
if requested, the model trained on the complete training data using the best parameter combination. |

`best.tune_wsvm()`

returns the best model detected by `tune_wsvm`

.

David Meyer

Modified by Tianchen Xu tx2155@columbia.edu

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
data(iris)
obj <- tune_wsvm(Species~., weight = c(rep(0.8, 50),rep(1,100)),
data = iris, ranges = list(gamma = 2^(-1:1), cost = 2^(2:4)),
tunecontrol = tune.control(sampling = "fix"))
set.seed(11)
obj <- tune_wsvm(Species~., weight = c(rep(1, 52),rep(0,98)),
data = iris, use_zero_weight = TRUE,
ranges = list(gamma = 2^(-1:1), cost = 2^(2:4)),
tunecontrol = tune.control(sampling = "bootstrap"))
summary(obj)
plot(obj, transform.x = log2, transform.y = log2)
plot(obj, type = "perspective", theta = 120, phi = 45)
best.tune_wsvm(Species~.,weight = c(rep(0.08, 50),rep(1,100)),
data = iris, ranges = list(gamma = 2^(-1:1), cost = 2^(2:4)),
tunecontrol = tune.control(sampling = "fix"))
``` |

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