Description Usage Arguments Format References See Also

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.

All supported measures can be found by `listMeasures`

or as a table
in the tutorial appendix: http://mlr-org.github.io/mlr-tutorial/release/html/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`

.

Most measures can directly be accessed via the function named after the scheme measureX (e.g. measureSSE).

For clustering measures, we compact the predicted cluster IDs such that they form a continuous series starting with 1. If this is not the case, some of the measures will generate warnings.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | ```
featperc
timetrain
timepredict
timeboth
sse
measureSSE(truth, response)
mse
measureMSE(truth, response)
rmse
measureRMSE(truth, response)
medse
measureMEDSE(truth, response)
sae
measureSAE(truth, response)
mae
measureMAE(truth, response)
medae
measureMEDAE(truth, response)
rsq
measureRSQ(truth, response)
expvar
measureEXPVAR(truth, response)
arsq
rrse
measureRRSE(truth, response)
rae
measureRAE(truth, response)
mape
measureMAPE(truth, response)
msle
measureMSLE(truth, response)
rmsle
kendalltau
measureKendallTau(truth, response)
spearmanrho
measureSpearmanRho(truth, response)
mmce
measureMMCE(truth, response)
acc
measureACC(truth, response)
ber
multiclass.aunu
measureAUNU(probabilities, truth)
multiclass.aunp
measureAUNP(probabilities, truth)
multiclass.au1u
measureAU1U(probabilities, truth)
multiclass.au1p
measureAU1P(probabilities, truth)
multiclass.brier
measureMulticlassBrier(probabilities, truth)
logloss
measureLogloss(probabilities, truth)
ssr
measureSSR(probabilities, truth)
qsr
measureQSR(probabilities, truth)
lsr
measureLSR(probabilities, truth)
kappa
measureKAPPA(truth, response)
wkappa
measureWKAPPA(truth, response)
auc
measureAUC(probabilities, truth, negative, positive)
brier
measureBrier(probabilities, truth, negative, positive)
brier.scaled
measureBrierScaled(probabilities, truth, negative, positive)
bac
measureBAC(truth, response, negative, positive)
tp
measureTP(truth, response, positive)
tn
measureTN(truth, response, negative)
fp
measureFP(truth, response, positive)
fn
measureFN(truth, response, negative)
tpr
measureTPR(truth, response, positive)
tnr
measureTNR(truth, response, negative)
fpr
measureFPR(truth, response, negative, positive)
fnr
measureFNR(truth, response, negative, positive)
ppv
measurePPV(truth, response, positive, probabilities = NULL)
npv
measureNPV(truth, response, negative)
fdr
measureFDR(truth, response, positive)
mcc
measureMCC(truth, response, negative, positive)
f1
gmean
measureGMEAN(truth, response, negative, positive)
gpr
measureGPR(truth, response, positive)
multilabel.hamloss
measureMultilabelHamloss(truth, response)
multilabel.subset01
measureMultilabelSubset01(truth, response)
multilabel.f1
measureMultiLabelF1(truth, response)
multilabel.acc
measureMultilabelACC(truth, response)
multilabel.ppv
measureMultilabelPPV(truth, response)
multilabel.tpr
measureMultilabelTPR(truth, response)
cindex
meancosts
mcp
db
dunn
G1
G2
silhouette
``` |

`truth` |
[ |

`response` |
[ |

`probabilities` |
[ |

`negative` |
[ |

`positive` |
[ |

none

He, H. & Garcia, E. A. (2009)
*Learning from Imbalanced Data.*
IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9. pp. 1263-1284.

Other performance: `ConfusionMatrix`

,
`calculateConfusionMatrix`

,
`calculateROCMeasures`

,
`estimateRelativeOverfitting`

,
`makeCostMeasure`

,
`makeCustomResampledMeasure`

,
`makeMeasure`

, `performance`

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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