This function takes a siamcat-class-object containing a model trained by train.model and performs predictions on a given test-set.

make.predictions(siamcat, siamcat.holdout = NULL,
    normalize.holdout = TRUE, verbose = 1)



object of class siamcat-class


optional, object of class siamcat-class on which to make predictions, defaults to NULL


boolean, should the holdout features be normalized with a frozen normalization (see normalize.features) using the normalization parameters in siamcat?, defaults to TRUE


integer, control output: 0 for no output at all, 1 for only information about progress and success, 2 for normal level of information and 3 for full debug information, defaults to 1


object of class siamcat-class with the slot pred_matrix filled


This functions uses the model in the model_list-slot of the siamcat object to make predictions on a given test set. The test set can either consist of the test instances in the cross- validation, saved in the data_split-slot of the same siamcat object, or a completely external feature set, given in the form of another siamcat object (siamcat.holdout).


data(siamcat_example) # Simple example siamcat.pred <- make.predictions(siamcat_example)
#> Made predictions successfully.
# Predictions on a holdout-set pred.mat <- make.predictions(siamcat.trained, siamcat.holdout, normalize.holdout=TRUE)
#> Error in make.predictions(siamcat.trained, siamcat.holdout, normalize.holdout = TRUE): object 'siamcat.holdout' not found