JointNets (version 2.0.1)

QDA_eval: graphical model model evaluation using QDA as a classifier

Description

graphical model model evaluation using QDA as a classifier

Usage

QDA_eval(train, valid, test, lambda_range, v_seeking_length = 10,
  method = "diffee", ...)

Arguments

train

a list of training data

valid

a list of validation data

test

a list of test data

lambda_range

a vector of lambda values to train to given method, eg c(0.1,0.2,0.3)

v_seeking_length

second hyperparameter length, default to 10

method

name of the method to be evaluated

...

optional parameters passed to your method from JointNets package

Value

covriance matrix / kendall tau correlation matrix

Examples

# NOT RUN {
library(JointNets)
data("nip_37_data")
split = train_valid_test_split(nip_37_data,c(0.8,0.1,0.1),10000)
train = split[["train"]]
valid = split[["valid"]]
test = split[["test"]]
v_seeking_length = 2
lambda_range = seq(0.5,1, length.out = 2)
result = QDA_eval(train,valid,test,lambda_range, v_seeking_length, method = "diffee")
result[["best test accuracy"]]
# }