Description Usage Arguments Value Note Author(s) References Examples
Classification accuracy measures for pcc, kappa, users accuracy, producers accuracy
1 | accuracy(x, y)
|
x |
vector of predicted data or table/matrix contingency table |
y |
vector of observed data, if x is not table/matrix contingency table |
A list class object with the following components:
PCC percent correctly classified (accuracy)
auc Area Under the ROC Curve
users.accuracy The users accuracy
producers.accuracy The producers accuracy
kappa Cohen's Kappa (chance corrected accuracy)
true.skill Hanssen-Kuiper skill score (aka true score statistic)
sensitivity Sensitivity (aka, recall)
specificity Specificity
plr Positive Likelihood Ratio
nlr Negative Likelihood Ratio
typeI.error Type I error (omission)
typeII.error Type II error (commission)
gini Gini entropy index
f.score F-score
gain Information gain (aka precision)
mcc Matthew's correlation
confusion A confusion matrix
sensitivity = true positives / ( true positives + false positives )
specificity = true negatives / ( true negatives + false positives )
Type I error = 1 - specificity
Type II error = 1 - sensitivity
Positive Likelihood Ratio = sensitivity / (1 - specificity)
Negative Likelihood Ratio = (1 - sensitivity) / specificity
gain = sensitivity / ( (true positives + true negatives) / n )
auc = (tpr - fpr + 1) / 2
F-Score = 2 * (precision * recall) / (precision + recall)
Hanssen-Kuiper skill score (aka true score statistic) = [(tp * tn) - (fp * fn)] / [(tp + fn) + (fp + tn)], The true skill score has an expected -1 to +1, with 0 representing no discrimination.
Using the table function matrix positions for a 2x2 confusion matrix are TP(1), FN(3), FP(2), TN(4)
Jeffrey S. Evans <jeffrey_evans<at>tnc.org>
Cohen, J. (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20 (1):37-46 Cohen, J. (1968) Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin 70 (4):213-220 Powers, D.M.W., (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation. Journal of Machine Learning Technologies 2(1):37-63.
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