crossvalidate: Cross-validate on a matrix of ToF data with labels
In JimSkinner/toftools: A set of tools for dealing with ToF data

Description Usage Arguments Details Value

Perform 10-fold cross-validation on a matrix of ToF data. This produces a 'caret::train' object containing lots of relevant information. One can extract predictions made and use these to determine accuracy.

1 2	crossvalidate(data_matrix, labels, model = "xgbTree", n_folds = 10, tune = FALSE, hyperparams = "tof")

`data_matrix`	A matrix of ToF data
`labels`	A character vector of class labels (shoulkd have 2 unique values)
`model`	The model to use
`n_folds`	number of folds in crossvalidation
`tune`	Whether to tune hyper-parameters (TRUE), or use defaults (FALSE)
`hyperparams`	One of 'tof' or 'gcims' whether the default hyper- parameters have been picked for tof data or gc-ims data. No effect if tune = TRUE.

The caret::train object also includes a classifier trained on the full dataset. #' This can be used to make further predictions on new data, such as a validation set.

A caret::train object

JimSkinner/toftools documentation built on Oct. 30, 2019, 7:55 p.m.