Perform generic out-of-bag error analysis.

Share:

Description

If performing regression, calculate which out-of-bag residuals and MSE. Otherwise, calculate which out-of-bag observations were classified correctly, what the overall misclassification rate is, as well as the confusion matrix.

Usage

1
defaultOOBPerformanceAnalysis(prediction, response, oobObs)

Arguments

prediction

a vector of predicted responses.

response

a vector of true response.

oobObs

a vector of indices which values in predictions are of out-of-bag observations.

Value

If performing regression, return a list with components:

oobMSE

the out-of-bag mean squared error.

resVec

a vector of length nrow(data) whose entries correspond to observations in data. The entry has values NA if the observation was not out-of-bag, and the difference between the predicted and true response (the residual) if the observation was out-of-bag.

Otherwise, return a list with components:

oobErr

overall misclassification rate.

oobConfMat

the confusion matrix of out-of-bag predictions against the true class labels.

errVec

a vector of length nrow(data) whose entries correspond to observations in data. The entry has values NA if the observation was not out-of-bag, and a 1 or 0 depending whether estimator failed to correctly classify the observation.

See Also

Other performance analyzers: boost, boost.function, boost.list

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.