Model metrics
Common model/evaluation metrics for machine learning.
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)
actual |
Vector of actual target values. |
predicted |
Vector of predicted target values. |
na.rm |
Logical indicating whether or not |
The metric_auc and metric_logLoss functions are based on
code from the Metrics
package.
x <- rnorm(10) y <- rnorm(10) metric_mse(x, y) metric_rsquared(x, y)
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