DMTL (version 0.1.2)

performance: Evaluate Regression Model Performance using Various Metrics

Description

This function produces the predictive performance for a regression model using various common performance metrics such as MSE, R-squared, or Correlation coefficients.

Usage

performance(y_obs, y_pred, measures = c("NRMSE", "NMAE", "PCC"))

Arguments

y_obs

Observed response values

y_pred

Predicted response values

measures

Performance measures. One can specify a single measure or a vector containing multiple measures in terms of common error or similarity metrics. The available options are roughly divided into 3 categories -

• "MSE", "RMSE", "NRMSE" for mean squared error, root mean squared error, and normalized root mean squared error, respectively.

• "MAE", "NMAE" for mean absolute error, and normalized mean absolute error, respectively.

• "PCC", "SCC", "RSQ" for Pearson's correlation, Spearman's correlation, and R-squared, respectively.

Defaults to c("NRMSE", "NMAE", "PCC").

Value

A vector containing the performance metric values.

Examples

# NOT RUN {
set.seed(654321)
x <- rnorm(1000, 0.2, 0.5)
y <- x^2 + rnorm(1000, 0, 0.1)
y_fit <- predict(lm(y ~ x))
print(performance(y, y_fit, measures = c("MSE", "RSQ")))

# }