perfScores {MKclass}

Compute Performance Scores for Binary Classification

Description

The function computes various performance scores for binary classification.

Usage

perfScores(pred, truth, namePos, wBS = 0.5, scores = "all", transform = FALSE)

Arguments

pred

numeric values that shall be used for classification; e.g. probabilities to belong to the positive group.

truth

true grouping vector or factor.

namePos

value representing the positive group.

wBS

weight used for computing the weighted Brier score (BS), where postive BS is multiplied by wBS and negative BS by 1-wBS. Must be in [0,1].

scores

character vector giving the scores that shall be computed; see details. Default "all" computes all scores available.

transform

logical value indicating whether the values in pred should be transformed to [0,1]; see details.

Details

The function perfScores can be used to compute various performance scores. For computing specific scores, the abbreviation given in parentheses have to be specified in argument scores. Single scores can also be computed by respective functions, where their names are identical to the abbreviations given in the parentheses.

The available scores are: area under the ROC curve (AUC), Gini index (GINI), Brier score (BS), positive Brier score (PBS), negative Brier score (NBS), weighted Brier score (WBS), balanced Brier score (BBS), Brier skill score (BSS).

If the predictions (pred) are not in the interval [0,1], the various Brier scores are not valid. By setting argument transform to TRUE, a simple logistic regression model is fit to the provided data and the predicted values are used for the computations.

Value

data.frame with names of the scores and their respective values.

Author(s)

Matthias Kohl [e-mail]

References

G.W. Brier (1950). Verification of forecasts expressed in terms of probability. Mon. Wea. Rev. 78, 1-3.

T. Fawcett (2006). An introduction to ROC analysis. Pattern Recognition Letters 27, 861-874.

T.A. Gerds, T. Cai, M. Schumacher (2008). The performance of risk prediction models. Biom J 50, 457-479.

D. Hand, R. Till (2001). A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine Learning 45, 171-186.

J. Hernandez-Orallo, P.A. Flach, C. Ferri (2011). Brier curves: a new cost- based visualisation of classifier performance. In L. Getoor and T. Scheffer (eds.) Proceedings of the 28th International Conference on Machine Learning (ICML-11), 585???592 (ACM, New York, NY, USA).

J. Hernandez-Orallo, P.A. Flach, C. Ferri (2012). A unified view of performance metrics: Translating threshold choice into expected classification loss. J. Mach. Learn. Res. 13, 2813-2869.

B.W. Matthews (1975). Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochimica et Biophysica Acta (BBA) - Protein Structure 405, 442-451.

See Also

perfMeasures

Examples

## example from dataset infert
fit <- glm(case ~ spontaneous+induced, data = infert, family = binomial())
pred <- predict(fit, type = "response")

## with group numbers
perfScores(pred, truth = infert$case, namePos = 1)

## with group names
my.case <- factor(infert$case, labels = c("control", "case"))
perfScores(pred, truth = my.case, namePos = "case")

## on the scale of the linear predictors
pred2 <- predict(fit)
perfScores(pred2, truth = infert$case, namePos = 1)

## using weights
perfScores(pred, truth = infert$case, namePos = 1, wBS = 0.3)