measure_accuracy | R Documentation |
Classification accuracy on test set and other performance measure that can be used in CrossValidationSSL
and LearningCurveSSL
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)
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 |
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
Other RSSL utilities:
LearningCurveSSL()
,
SSLDataFrameToMatrices()
,
add_missinglabels_mar()
,
df_to_matrices()
,
missing_labels()
,
split_dataset_ssl()
,
split_random()
,
true_labels()
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