calcPredictionAccuracy

0th

Percentile

Calculate the Prediction Error for a Recommendation

Calculate prediction accuracy. For predicted ratings MAE (mean average error), MSE (means squared error) and RMSE (root means squared error) are calculated. For topNLists various binary classification metrics are returned.

Usage
calcPredictionAccuracy(x, data, ...) "calcPredictionAccuracy"(x, data, byUser=FALSE,...) "calcPredictionAccuracy"(x, data, byUser=FALSE, given=NULL, goodRating=NA,...) "calcPredictionAccuracy"(x, data, byUser=FALSE, given=NULL,...)
Arguments
x
Predicted items in a "topNList" or predicted ratings as a "realRatingMatrix"
data
Actual ratings by the user as a "RatingMatrix"
byUser
logical; Should the errors be averaged by user or over all recommendations?
given
how many items were given to create the predictions.
goodRating
threshold for determining what rating is a good rating. Used only if x is a topNList and data is a "realRatingMatrix".
...
further arguments.
Value

Returns a vector with the average measures for byUser=TRUE. Otherwise, a matrix with a row for each user is returned.

References

Asela Gunawardana and Guy Shani (2009). A Survey of Accuracy Evaluation Metrics of Recommendation Tasks, Journal of Machine Learning Research 10, 2935-2962.

See Also

topNList, binaryRatingMatrix, realRatingMatrix.

Aliases
  • calcPredictionAccuracy
  • calcPredictionAccuracy,realRatingMatrix,realRatingMatrix-method
  • calcPredictionAccuracy,topNList,binaryRatingMatrix-method
  • calcPredictionAccuracy,topNList,realRatingMatrix-method
Examples
### real valued recommender  
data(Jester5k)

## create 90/10 split (known/unknown) for the first 500 users in Jester5k
e <- evaluationScheme(Jester5k[1:500,], method="split", train=0.9, 
    k=1, given=15)
e

## create a user-based CF recommender using training data
r <- Recommender(getData(e, "train"), "UBCF")

## create predictions for the test data using known ratings (see given above)
p <- predict(r, getData(e, "known"), type="ratings")
p

## compute error metrics averaged per user and then averaged over all 
## recommendations
calcPredictionAccuracy(p, getData(e, "unknown"))
head(calcPredictionAccuracy(p, getData(e, "unknown"), byUser=TRUE))

## evaluate topNLists instead (you need to specify given and goodRating!)
p <- predict(r, getData(e, "known"), type="topNList")
p
calcPredictionAccuracy(p, getData(e, "unknown"), given=15, goodRating=5) 
  
## evaluate a binary recommender
data(MSWeb)
MSWeb10 <- sample(MSWeb[rowCounts(MSWeb) >10,], 50)

e <- evaluationScheme(MSWeb10, method="split", train=0.9, 
    k=1, given=3)
e
  
## create a user-based CF recommender using training data
r <- Recommender(getData(e, "train"), "UBCF")
  
## create predictions for the test data using known ratings (see given above)
p <- predict(r, getData(e, "known"), type="topNList", n=10)
p

calcPredictionAccuracy(p, getData(e, "unknown"), given=3)
Documentation reproduced from package recommenderlab, version 0.2-1, License: GPL-2

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