View source: R/accuracyOverall.R
accuracyOverall | R Documentation |
Find overall accuracy.
accuracyOverall(predicted, actual, dropUndefined = FALSE)
wisdomOfCrowd(predicted, actual, dropUndefined = FALSE)
predicted |
vector of continuous predicted values. |
actual |
vector of actual values. |
dropUndefined |
|
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!
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
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()
# Prepare Data
data("USArrests")
# Calculate Accuracy
accuracyOverall(predicted = USArrests$Assault, actual = USArrests$Murder)
wisdomOfCrowd(predicted = USArrests$Assault, actual = 200)
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