deepboost (version 0.1.5)

deepboost.gridSearch: Returns optimised parameter list for deepboost model on given data

Description

Returns optimised parameter list for deepboost model on given data

Usage

deepboost.gridSearch(formula, data, k = 10, seed = 666, logging_level = 1)

Arguments

formula

A R Formula object see : ?formula

data

input data.frame as training for model

k

number of folds (default = 10) for cross validation optimisation

seed

for random split to train / test (default 666)

logging_level

print extra data while training 0 - no data, 1 - gridSearch data (default), 2 - all data

Value

vector with average accuracy for chosen parameters, and a list of the best parameter combination: (accuracy, (num_iter, beta, lambda, loss_type))

Details

Finds optimised parameters for deepboost training. using grid search techniques over: - predefined, battle tested parameter possible values - cross validation over k folds

Examples

deepboost.gridSearch(y ~ .,
 data.frame(x1=rep(c(0,0,1,1),2),x2=rep(c(0,1,0,1),2),y=factor(rep(c(0,0,0,1),2))), k=2)