| Type: | Package |
| Title: | Performance Metrics in Predictive Modeling |
| Version: | 0.1.0 |
| Maintainer: | Pankaj Das <pankaj.das2@icar.gov.in> |
| Description: | Performance metric provides different performance measures like mean squared error, root mean square error, mean absolute deviation, mean absolute percentage error etc. of a fitted model. These can provide a way for forecasters to quantitatively compare the performance of competing models. For method details see (i) Pankaj Das (2020) http://krishi.icar.gov.in/jspui/handle/123456789/44138. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2024-06-14 07:06:05 UTC; Pankaj |
| Author: | Pankaj Das  [aut,
cre] |
| Repository: | CRAN |
| Date/Publication: | 2024-06-14 15:20:11 UTC |
Coefficient of Variation of Root Mean Squared Error
Description
Coefficient of Variation of Root Mean Squared Error
Usage
CVRMSE(actual, predicted)
Arguments
actual |
Actual value of the target variable |
predicted |
Predicted/forecasted value of the target variable |
Value
CVRMSE of the fitted model
Examples
actual <- c(100, 150, 200, 250, 300, 350, 400, 450, 500, 550)
predicted <- c(95, 148, 210, 245, 290, 360, 395, 440, 510, 540)
CVRMSE(actual, predicted)
Mean Absolute Error
Description
Mean Absolute Error
Usage
MAE(actual, predicted)
Arguments
actual |
Actual value of the target variable |
predicted |
Predicted/forecasted value of the target variable |
Value
MAE of the fitted model
Examples
actual <- c(100, 150, 200, 250, 300, 350, 400, 450, 500, 550)
predicted <- c(95, 148, 210, 245, 290, 360, 395, 440, 510, 540)
MAE(actual, predicted)
Mean Absolute Percentage Error
Description
Mean Absolute Percentage Error
Usage
MAPE(actual, predicted)
Arguments
actual |
Actual value of the target variable |
predicted |
Predicted value of the target variable |
Value
MAPE of the fitted model
Examples
actual <- c(100, 150, 200, 250, 300, 350, 400, 450, 500, 550)
predicted <- c(95, 148, 210, 245, 290, 360, 395, 440, 510, 540)
MAPE(actual, predicted)
Maximum Error
Description
Maximum Error
Usage
ME(actual, predicted)
Arguments
actual |
Actual value of the target variable |
predicted |
Predicted value of the target variable |
Value
ME of the fitted model
Examples
actual <- c(100, 150, 200, 250, 300, 350, 400, 450, 500, 550)
predicted <- c(95, 148, 210, 245, 290, 360, 395, 440, 510, 540)
ME(actual, predicted)
Normalised Root Mean Squared Error
Description
Normalised Root Mean Squared Error
Usage
NRMSE(actual, predicted)
Arguments
actual |
Actual value of the target variable |
predicted |
Predicted value of the target variable |
Value
NRMSE of the fitted model
Examples
actual <- c(100, 150, 200, 250, 300, 350, 400, 450, 500, 550)
predicted <- c(95, 148, 210, 245, 290, 360, 395, 440, 510, 540)
NRMSE(actual, predicted)
Coefficient of Determination (R-Square)
Description
Coefficient of Determination (R-Square)
Usage
R2(actual, predicted)
Arguments
actual |
Actual value of the target variable |
predicted |
Predicted value of the target variable |
Value
Coefficient of Determination (R-Square) of the fitted model
Examples
actual <- c(100, 150, 200, 250, 300, 350, 400, 450, 500, 550)
predicted <- c(95, 148, 210, 245, 290, 360, 395, 440, 510, 540)
R2(actual, predicted)
Root Mean Squared Error
Description
Root Mean Squared Error
Usage
RMSE(actual, predicted)
Arguments
actual |
Actual value of the target variable |
predicted |
Predicted value of the target variable |
Value
RMSE and MSE of the fitted model
Examples
actual <- c(100, 150, 200, 250, 300, 350, 400, 450, 500, 550)
predicted <- c(95, 148, 210, 245, 290, 360, 395, 440, 510, 540)
RMSE(actual, predicted)
Accuracy of Model
Description
Accuracy of Model
Usage
accuracy(actual, predicted)
Arguments
actual |
Actual value of the target variable |
predicted |
Predicted/forecasted value of the target variable |
Value
Accuracy of the fitted model
Examples
actual <- c(100, 150, 200, 250, 300, 350, 400, 450, 500, 550)
predicted <- c(95, 148, 210, 245, 290, 360, 395, 440, 510, 540)
accuracy(actual, predicted)