Overall Accuracy.

Find overall accuracy.

Usage

accuracyOverall(predicted, actual, dropUndefined = FALSE)

wisdomOfCrowd(predicted, actual, dropUndefined = FALSE)

Arguments

predicted

vector of continuous predicted values.

actual

vector of actual values.

dropUndefined

TRUE or FALSE, indicating whether to drop any undefined values calculated with the accuracy indices.

Value

• ME = mean error

• MAE = mean absolute error

• MSE = mean squared error

• RMSE = root mean squared error

• MPE = mean percentage error

• MAPE = mean absolute percentage error

• sMAPE = symmetric mean absolute percentage error

• MASE = mean absolute scaled error

• RMSLE = root mean squared log error

• rsquared = R-squared

• rsquaredAdj = adjusted R-squared

• rsquaredPredictive = predictive R-squared

Details

Compute overall accuracy estimates of predicted values in relation to actual values. Estimates of overall accuracy span all cutoffs. Some accuracy estimates can be undefined under various circumstances. Optionally, you can drop undefined values in the calculation of accuracy indices. Note that dropping undefined values changes the meaning of these indices. Use this option at your own risk!

See also

Mean absolute scaled error (MASE):
https://stats.stackexchange.com/questions/108734/alternative-to-mape-when-the-data-is-not-a-time-series
https://stats.stackexchange.com/questions/322276/is-mase-specified-only-to-time-series-data
https://stackoverflow.com/questions/31197726/calculate-mase-with-cross-sectional-non-time-series-data-in-r
https://stats.stackexchange.com/questions/401759/how-can-mase-mean-absolute-scaled-error-score-value-be-interpreted-for-non-tim

Predictive R-squared:
https://www.r-bloggers.com/2014/05/can-we-do-better-than-r-squared/

Other accuracy: accuracyAtCutoff(), accuracyAtEachCutoff(), nomogrammer(), optimalCutoff(), posttestOdds()

Examples

# Prepare Data
data("USArrests")

# Calculate Accuracy
accuracyOverall(predicted = USArrests$Assault, actual = USArrests$Murder)
#>        ME     MAE      MSE    RMSE       MPE     MAPE    sMAPE    MASE    RMSLE
#> 1 162.972 162.972 32814.24 181.147 -2382.823 2382.823 91.16591 44.6861 2.990233
#>    rsquared rsquaredAdj rsquaredPredictive
#> 1 0.6430008   0.6355633          0.6163406
wisdomOfCrowd(predicted = USArrests$Assault, actual = 200)
#> Warning: essentially perfect fit: summary may be unreliable
#> Warning: essentially perfect fit: summary may be unreliable
#> Warning: ANOVA F-tests on an essentially perfect fit are unreliable
#> Warning: essentially perfect fit: summary may be unreliable
#> Warning: essentially perfect fit: summary may be unreliable
#> Warning: ANOVA F-tests on an essentially perfect fit are unreliable
#>                   ME   MAE       MSE     RMSE   MPE  MAPE     sMAPE MASE
#> individual    -29.24 76.32 7661.2400 87.52851 14.62 38.16 23.239460  Inf
#> crowdAveraged -29.24 29.24  854.9776 29.24000 14.62 14.62  7.886503  Inf
#>                   RMSLE bracketingRate
#> individual    0.6310922      0.4808163
#> crowdAveraged 0.1572068      0.4808163