mlr (version 1.1-18)

measures: Performance measures.

Description

A performance measure is evaluated after a single train/predict step and returns a single number to assess the quality of the prediction (or maybe only the model, think AIC). The measure itself knows whether it wants to be minimized or maximized and for what tasks it is applicable. See below for a list of already implemented measures. If you want a measure for a misclassification cost matrix, look at makeCostMeasure. If you want to implement your own measure, look at makeMeasure.

Usage

measures()
  featperc
  timetrain
  timepredict
  timeboth
  sse
  mse
  rmse
  medse
  sae
  mae
  medae
  mmce
  acc
  ber
  auc
  tp
  tn
  fp
  fn
  tpr
  tnr
  fpr
  fnr
  ppv
  npv
  fdr
  mcc
  f1
  gmean
  gpr

format

List of 12 $ id : chr "featperc" $ minimize : logi TRUE $ classif : logi TRUE $ regr : logi TRUE $ only.binary : logi FALSE $ allowed.pred.types: chr [1:2] "response" "prob" $ req.pred : logi TRUE $ req.model : logi TRUE $ req.task : logi FALSE $ fun :function (task, model, pred, extra.args) $ extra.args : list() $ aggr :List of 2 ..- attr(*, "class")= chr "Aggregation" - attr(*, "class")= chr "Measure"

code

ber

itemize

• timetrain

cr

• Sum of squared errors
• Mean of squared errors
• Median of squared errors
• Sum of absolute errors
• Mean of absolute errors
• Median of absolute errors
• Root mean square error
• Time of fitting the model
• Time of predicting test set
• timetrain + trainpredict
• Percentage of original features used for model, useful for feature selection.

item

• mse
• medse
• sae
• mae
• medae
• rmse
• timepredict
• timeboth
• featperc

Details

Classification:

• mmce

{ Mean misclassification error.} acc{ Accuracy.} ber{ Balanced error rate. Mean of misclassification error rates on all individual classes.} tp{ True positives.} tpr{ True positive rate, also called hit rate or recall.} fp{ False positives, also called false alarms.} fpr{ False positive rate, also called false alarm rate or fall-out.} tn{ True negatives, also called correct rejections.} tnr{ True negative rate. Also called specificity.} fn{ False negatives, also called misses.} fnr{ False negative rate.} ppv{ Positive predictive value, also called precision.} npv{ Negative predictive value.} fdr{ False discovery rate.} f1{ F1 measure.} mcc{ Matthews correlation coefficient.} gmean{ G-mean, geomentric mean of recall and specificity.} gpr{ Geometric mean of precision and recall.} auc{ Area under the curve.}