Description Usage Arguments Value Examples
graphical model model evaluation using QDA as a classifier
1 2 |
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 |
covriance matrix / kendall tau correlation matrix
1 2 3 4 5 6 7 8 9 10 | 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"]]
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