These are objects that can be used for modeling, especially in conjunction with the parsnip package.
dropoutepochs
activation
mixture
penalty
rbf_sigma
prod_degree
num_terms
num_comp
cost
scale_factor
margin
degree
deg_free
hidden_units
batch_size
Each object is generated by either new_quant_param or
new_qual_param.
An object of class quant_param (inherits from param) of length 7.
These objects are pre-made parameter sets that are useful when the model is based on some type of slope/intercept model.
penalty: The total amount of regularization used. This is used by
parsnip::linear_reg() and parsnip::logistic_reg() with glmnet models.
mixture: the proportion of L1 regularization in the model.
(parsnip::linear_reg() and parsnip::logistic_reg())
dropout: the parameter dropout rate. (parsnip:::mlp())
epochs: the number of iterations of training. (parsnip:::mlp())
activation: the type of activation function between network layers.
(parsnip:::mlp())
hidden_units: the number of hidden units in a network layer.
(parsnip:::mlp())
batch_size: the mini-batch size for neural networks.
rbf_sigma: the sigma parameters of a radial basis function.
cost: a cost value for SVM models.
scale_factor: the polynomial and hyperbolic tangent kernel scaling factor.
margin: the SVM margin parameter (e.g. epsilon in the insensitive-loss
function for regression).
degree: the polynomial degree.
prod_degree: the number of terms to combine into interactions. A value of
1 implies an additive model. Useful for MARS models.
num_terms: a nonspecific parameter for the number of terms in a model.
This can be used with models that include feature selection, such as MARS.
num_comp: the number of components in a model (e.g. PCA or PLS components).
deg_free: a parameter for the degrees of freedom.