metrics  R Documentation 
Compute measures of agreement between observed and predicted responses.
accuracy( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) auc( observed, predicted = NULL, weights = NULL, multiclass = c("pairs", "all"), metrics = c(MachineShop::tpr, MachineShop::fpr), stat = MachineShop::settings("stat.Curve"), ... ) brier(observed, predicted = NULL, weights = NULL, ...) cindex(observed, predicted = NULL, weights = NULL, ...) cross_entropy(observed, predicted = NULL, weights = NULL, ...) f_score( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), beta = 1, ... ) fnr( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) fpr( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) kappa2( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) npv( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) ppr( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) ppv( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) pr_auc( observed, predicted = NULL, weights = NULL, multiclass = c("pairs", "all"), ... ) precision( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) recall( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) roc_auc( observed, predicted = NULL, weights = NULL, multiclass = c("pairs", "all"), ... ) roc_index( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), fun = function(sensitivity, specificity) (sensitivity + specificity)/2, ... ) sensitivity( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) specificity( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) tnr( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) tpr( observed, predicted = NULL, weights = NULL, cutoff = MachineShop::settings("cutoff"), ... ) weighted_kappa2(observed, predicted = NULL, weights = NULL, power = 1, ...) gini(observed, predicted = NULL, weights = NULL, ...) mae(observed, predicted = NULL, weights = NULL, ...) mse(observed, predicted = NULL, weights = NULL, ...) msle(observed, predicted = NULL, weights = NULL, ...) r2( observed, predicted = NULL, weights = NULL, method = c("mse", "pearson", "spearman"), distr = character(), ... ) rmse(observed, predicted = NULL, weights = NULL, ...) rmsle(observed, predicted = NULL, weights = NULL, ...)
observed 
observed responses; or confusion, performance curve, or resample result containing observed and predicted responses. 
predicted 
predicted responses if not contained in

weights 
numeric vector of nonnegative case weights for the observed responses [default: equal weights]. 
cutoff 
numeric (0, 1) threshold above which binary factor
probabilities are classified as events and below which survival
probabilities are classified. If 
... 
arguments passed to or from other methods. 
multiclass 
character string specifying the method for computing
generalized area under the performance curve for multiclass factor
responses. Options are to average over areas for each pair of classes
( 
metrics 
vector of two metric functions or function names that define a curve under which to calculate area [default: ROC metrics]. 
stat 
function or character string naming a function to compute a
summary statistic at each cutoff value of resampled metrics in performance
curves, or 
beta 
relative importance of recall to precision in the calculation of

fun 
function to calculate a desired sensitivityspecificity tradeoff. 
power 
power to which positional distances of offdiagonals from the
main diagonal in confusion matrices are raised to calculate

method 
character string specifying whether to compute 
distr 
character string specifying a distribution with which to
estimate the observed survival mean in the total sum of square component of

Hand, D. J., & Till, R. J. (2001). A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine Learning, 45, 171186.
metricinfo
, performance
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