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MachineShop (version 2.8.0)

performance: Model Performance Metrics

Description

Compute measures of model performance.

Usage

performance(x, ...)

# S3 method for BinomialVariate performance( x, y, metrics = MachineShop::settings("metrics.numeric"), na.rm = TRUE, ... )

# S3 method for factor performance( x, y, metrics = MachineShop::settings("metrics.factor"), cutoff = MachineShop::settings("cutoff"), na.rm = TRUE, ... )

# S3 method for matrix performance( x, y, metrics = MachineShop::settings("metrics.matrix"), na.rm = TRUE, ... )

# S3 method for numeric performance( x, y, metrics = MachineShop::settings("metrics.numeric"), na.rm = TRUE, ... )

# S3 method for Surv performance( x, y, metrics = MachineShop::settings("metrics.Surv"), cutoff = MachineShop::settings("cutoff"), na.rm = TRUE, ... )

# S3 method for ConfusionList performance(x, ...)

# S3 method for ConfusionMatrix performance(x, metrics = MachineShop::settings("metrics.ConfusionMatrix"), ...)

# S3 method for Resamples performance(x, ...)

Arguments

x

observed responses; or confusion or resample result containing observed and predicted responses.

...

arguments passed from the Resamples method to the response type-specific methods or from the method for ConfusionList to ConfusionMatrix.

y

predicted responses if not contained in x.

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 NA when calculating metrics.

cutoff

numeric (0, 1) threshold above which binary factor probabilities are classified as events and below which survival probabilities are classified.

See Also

plot, summary

Examples

Run this code
# NOT RUN {
## 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)
# }
# NOT RUN {
# }

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