metrics: Performance Metrics

Description

Compute measures of agreement between observed and predicted responses.

Usage

accuracy(
  observed,
  predicted = NULL,
  weights = NULL,
  cutoff = MachineShop::settings("cutoff"),
  ...
)
auc(
  observed,
  predicted = NULL,
  weights = NULL,
  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"),
  ...
)
ppv(
  observed,
  predicted = NULL,
  weights = NULL,
  cutoff = MachineShop::settings("cutoff"),
  ...
)
pr_auc(observed, predicted = NULL, weights = NULL, ...)
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, ...)
roc_index(
  observed,
  predicted = NULL,
  weights = NULL,
  cutoff = MachineShop::settings("cutoff"),
  fun = function(sensitivity, specificity) (sensitivity + specificity)/2,
  ...
)
rpp(
  observed,
  predicted = NULL,
  weights = NULL,
  cutoff = MachineShop::settings("cutoff"),
  ...
)
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, distr = character(), ...)
rmse(observed, predicted = NULL, weights = NULL, ...)
rmsle(observed, predicted = NULL, weights = NULL, ...)

Arguments

observed

observed responses; or confusion, performance curve, or resample result containing observed and predicted responses.

predicted

predicted responses if not contained in observed.

weights

numeric vector of non-negative 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 NULL, then confusion matrix-based metrics are computed on predicted class probabilities if given.

...

arguments passed to or from other methods.

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 NULL for resample-specific metrics.

beta

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

fun

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.

distr

character string specifying a distribution with which to estimate the observed survival mean in the total sum of square component of r2. Possible values are "empirical" for the Kaplan-Meier estimator, "exponential", "extreme", "gaussian", "loggaussian", "logistic", "loglogistic", "lognormal", "rayleigh", "t", or "weibull". Defaults to the distribution that was used in predicting mean survival times.

Description

Usage

Arguments

See Also