rm_class-class {randomMachines}

S4 class for RM classification

Description

S4 class for RM classification

Details

For more details see Ara, Anderson, et al. "Random machines: A bagged-weighted support vector model with free kernel choice." Journal of Data Science 19.3 (2021): 409-428.

Slots

train

a data.frame corresponding to the training data used into the model

class_name

a string with target variable used in the model

kernel_weight

a numeric vector corresponding to the weights for each bootstrap model contribution

lambda_values

a named list with value of the vector of \boldsymbol{\lambda} sampling probabilities associated with each each kernel function

model_params

a list with all used model specifications

bootstrap_models

a list with all ksvm objects for each bootstrap sample

bootstrap_samples

a list with all bootstrap samples used to train each base model of the ensemble

prob

a boolean indicating if a probabilitistic approch was used in the classification Random Machines