measure_accuracy {RSSL}

Performance measures used in classifier evaluation

Description

Classification accuracy on test set and other performance measure that can be used in CrossValidationSSL and LearningCurveSSL

Usage

measure_accuracy(trained_classifier, X_l = NULL, y_l = NULL, X_u = NULL,
  y_u = NULL, X_test = NULL, y_test = NULL)

measure_error(trained_classifier, X_l = NULL, y_l = NULL, X_u = NULL,
  y_u = NULL, X_test = NULL, y_test = NULL)

measure_losstest(trained_classifier, X_l = NULL, y_l = NULL, X_u = NULL,
  y_u = NULL, X_test = NULL, y_test = NULL)

measure_losslab(trained_classifier, X_l = NULL, y_l = NULL, X_u = NULL,
  y_u = NULL, X_test = NULL, y_test = NULL)

measure_losstrain(trained_classifier, X_l = NULL, y_l = NULL, X_u = NULL,
  y_u = NULL, X_test = NULL, y_test = NULL)

Arguments

trained_classifier

the trained classifier object

X_l

design matrix with labeled object

y_l

labels of labeled objects

X_u

design matrix with unlabeled object

y_u

labels of unlabeled objects

X_test

design matrix with test object

y_test

labels of test objects

Functions

• measure_error(): Classification error on test set

• measure_losstest(): Average Loss on test objects

• measure_losslab(): Average loss on labeled objects

• measure_losstrain(): Average loss on labeled and unlabeled objects

See Also

Other RSSL utilities: LearningCurveSSL(), SSLDataFrameToMatrices(), add_missinglabels_mar(), df_to_matrices(), missing_labels(), split_dataset_ssl(), split_random(), true_labels()