Validation and out-of-sample testing
These are all the available library functions that users have available for validation and out-of-sample testing. Refer to the corresponding usage page to see examples and explanations behind these functions.
EmpiricalRiskMinimization.lambda
— Method.lambda(M)
returns the lambda
used during training. If multiple lambda
values were specified, returns the optimal one
EmpiricalRiskMinimization.lambdaopt
— Method.lambdaopt(M)
returns the optimal lambda computed during training of M
EmpiricalRiskMinimization.lambdapath
— Method.lambdapath(M)
returns the vector of lambdas used
EmpiricalRiskMinimization.testloss
— Method.testloss(M)
returns the average testing loss
EmpiricalRiskMinimization.testlosspath
— Method.testlosspath(M)
returns the vector of test losses at various lambdas
EmpiricalRiskMinimization.thetaopt
— Method.thetaopt(M)
returns the optimal theta
chosen during training
EmpiricalRiskMinimization.thetapath
— Method.thetapath(M)
returns the vector of optimal thetas at various lambdas
EmpiricalRiskMinimization.trainloss
— Method.trainloss(M)
returns the average training loss
EmpiricalRiskMinimization.trainlosspath
— Method.trainlosspath(M)
returns the vector of train losses at various lambdas