performance {MachineShop} | R Documentation |
Compute measures of model performance.
performance(x, ...) ## S3 method for class 'BinomialVariate' performance( x, y, metrics = MachineShop::settings("metrics.numeric"), na.rm = TRUE, ... ) ## S3 method for class 'factor' performance( x, y, metrics = MachineShop::settings("metrics.factor"), cutoff = MachineShop::settings("cutoff"), na.rm = TRUE, ... ) ## S3 method for class 'matrix' performance( x, y, metrics = MachineShop::settings("metrics.matrix"), na.rm = TRUE, ... ) ## S3 method for class 'numeric' performance( x, y, metrics = MachineShop::settings("metrics.numeric"), na.rm = TRUE, ... ) ## S3 method for class 'Surv' performance( x, y, metrics = MachineShop::settings("metrics.Surv"), cutoff = MachineShop::settings("cutoff"), na.rm = TRUE, ... ) ## S3 method for class 'ConfusionList' performance(x, ...) ## S3 method for class 'ConfusionMatrix' performance(x, metrics = MachineShop::settings("metrics.ConfusionMatrix"), ...) ## S3 method for class 'Resamples' performance(x, ...)
x |
observed responses; or confusion or resample result containing observed and predicted responses. |
... |
arguments passed from the |
y |
predicted responses if not contained in |
metrics |
metric function, function name, or vector of these with which to calculate performance. |
na.rm |
logical indicating whether to remove observed or predicted
responses that are |
cutoff |
numeric (0, 1) threshold above which binary factor probabilities are classified as events and below which survival probabilities are classified. |
## Requires prior installation of suggested package gbm to run res <- resample(Species ~ ., data = iris, model = GBMModel) (perf <- performance(res)) summary(perf) plot(perf) ## Survival response example library(survival) gbm_fit <- fit(Surv(time, status) ~ ., data = veteran, model = GBMModel) obs <- response(gbm_fit, newdata = veteran) pred <- predict(gbm_fit, newdata = veteran, type = "prob") performance(obs, pred)