MachineShop (version 1.1.0)

metrics: Performance Metrics

Description

Compute measures of agreement between observed and predicted responses.

Usage

accuracy(observed, predicted = NULL, cutoff = 0.5, times = numeric(),
  ...)
brier(observed, predicted = NULL, times = numeric(), ...)
cindex(observed, predicted = NULL, ...)
cross_entropy(observed, predicted = NULL, ...)
f_score(observed, predicted = NULL, cutoff = 0.5, times = numeric(),
  beta = 1, ...)
gini(observed, predicted = NULL, ...)
kappa2(observed, predicted = NULL, cutoff = 0.5, times = numeric(),
  ...)
mae(observed, predicted = NULL, ...)
mse(observed, predicted = NULL, ...)
msle(observed, predicted = NULL, ...)
npv(observed, predicted = NULL, cutoff = 0.5, times = numeric(), ...)
ppv(observed, predicted = NULL, cutoff = 0.5, times = numeric(), ...)
pr_auc(observed, predicted = NULL, times = numeric(), ...)
precision(observed, predicted = NULL, cutoff = 0.5,
  times = numeric(), ...)
r2(observed, predicted = NULL, ...)
recall(observed, predicted = NULL, cutoff = 0.5, times = numeric(),
  ...)
rmse(observed, predicted = NULL, ...)
rmsle(observed, predicted = NULL, ...)
roc_auc(observed, predicted = NULL, times = numeric(), ...)
roc_index(observed, predicted = NULL, cutoff = 0.5,
  times = numeric(), f = function(sens, spec) sens + spec, ...)
sensitivity(observed, predicted = NULL, cutoff = 0.5,
  times = numeric(), ...)
specificity(observed, predicted = NULL, cutoff = 0.5,
  times = numeric(), ...)
weighted_kappa2(observed, predicted = NULL, power = 1, ...)

Arguments

observed

observed responses or ConfusionMatrix of observed and predicted responses.

predicted

predicted responses.

cutoff

threshold above which binary factor probabilities are classified as events and below which survival probabilities are classified.

times

numeric vector of follow-up times at which survival events were predicted.

...

arguments passed to or from other methods.

beta

relative importance of recall to precision in the calculation of f_score [default: F1 score].

f

function to calculate a desired sensitivity-specificity tradeoff.

power

power to which positional distances of off-diagonals from the main diagonal in confusion matrices are raised to calculate weighted_kappa2.

See Also

metricinfo, confusion, performance