vip (version 0.2.2)

metric_mse: Model metrics

Description

Common model/evaluation metrics for machine learning.

Usage

metric_mse(actual, predicted, na.rm = FALSE)
metric_rmse(actual, predicted, na.rm = FALSE)
metric_sse(actual, predicted, na.rm = FALSE)
metric_mae(actual, predicted, na.rm = FALSE)
metric_rsquared(actual, predicted, na.rm = FALSE)
metric_accuracy(actual, predicted, na.rm = FALSE)
metric_error(actual, predicted, na.rm = FALSE)
metric_auc(actual, predicted)
metric_logLoss(actual, predicted)
metric_mauc(actual, predicted)

Arguments

actual

Vector of actual target values.

predicted

Vector of predicted target values.

na.rm

Logical indicating whether or not NA values should be stripped before the computation proceeds.

Examples

# NOT RUN {
x <- rnorm(10)
y <- rnorm(10)
metric_mse(x, y)
metric_rsquared(x, y)
# }