The environment() measures keeps track of all measures in this package.
It stores meta information such as minimum, maximum or if the
measure must be minimized or maximized.
The following information is available for each measure:
id: Name of the measure.title: Short descriptive title.type:"binary"for binary classification,"classif"for binary or multi-class classification,"regr"for regression and"similarity"for similarity measures.lower: lower bound.upper: upper bound.predict_type: prediction type the measure operates on."response"corresponds to class labels for classification and the numeric response for regression."prob"corresponds to class probabilities, provided as a matrix with class labels as column names."se"corresponds to to the vector of predicted standard errors for regression.minimize: IfTRUEorFALSE, the objective is to minimize or maximize the measure, respectively. Can also beNA.obs_loss: Name of the function which is called to calculate the (unaggregated) loss per observation.trafo: Optionallist()of length 2, containing a transformation"fn"and its derivative"deriv".aggregated: IfTRUE, this function aggregates the losses to a single numeric value. Otherwise, a vector of losses is returned.sample_weights: IfTRUE, it is possible calculate a weighted measure.
Examples
names(measures)
#> [1] "rmsle" "one_zero" "mauc_au1u" "rse" "bbrier"
#> [6] "msle" "maxae" "zero_one" "tnr" "sse"
#> [11] "pbias" "mauc_aunp" "precision" "smape" "fomr"
#> [16] "mae" "phi" "tn" "ape" "mauc_aunu"
#> [21] "pinball" "fnr" "tp" "srho" "ae"
#> [26] "rsq" "jaccard" "npv" "sle" "dor"
#> [31] "bacc" "ppv" "medse" "auc" "rae"
#> [36] "sae" "rmse" "ktau" "tpr" "mcc"
#> [41] "linex" "fn" "maxse" "gmean" "fp"
#> [46] "bias" "fdr" "prauc" "logloss" "mauc_mu"
#> [51] "mse" "fpr" "mbrier" "ce" "recall"
#> [56] "gpr" "specificity" "se" "rrse" "acc"
#> [61] "fbeta" "medae" "sensitivity" "mape" "mauc_au1p"
measures$tpr
#> $id
#> [1] "tpr"
#>
#> $title
#> [1] "True Positive Rate"
#>
#> $type
#> [1] "binary"
#>
#> $lower
#> [1] 0
#>
#> $upper
#> [1] 1
#>
#> $predict_type
#> [1] "response"
#>
#> $minimize
#> [1] FALSE
#>
#> $obs_loss
#> [1] NA
#>
#> $aggregated
#> [1] TRUE
#>
#> $sample_weights
#> [1] FALSE
#>
#> $trafo
#> [1] NA
#>
as.data.frame(measures)
#> id title type lower
#> 1 rmsle Root Mean Squared Log Error regr 0
#> 2 one_zero One-Zero Utility Function classif 0
#> 3 mauc_au1u Average 1 vs. 1 multiclass AUC classif 0
#> 4 rse Relative Squared Error regr 0
#> 5 bbrier Binary Brier Score binary 0
#> 6 msle Mean Squared Log Error regr 0
#> 7 maxae Max Absolute Error regr 0
#> 8 zero_one Zero-One Classification Loss classif 0
#> 9 tnr True Negative Rate binary 0
#> 10 sse Sum of Squared Errors regr 0
#> 11 pbias Percent Bias regr -Inf
#> 12 mauc_aunp Weighted average 1 vs. rest multiclass AUC classif 0
#> 13 precision Precision binary 0
#> 14 smape Symmetric Mean Absolute Percent Error regr 0
#> 15 fomr False Omission Rate binary 0
#> 16 mae Mean Absolute Error regr 0
#> 17 phi Phi Coefficient Similarity similarity -1
#> 18 tn True Negatives binary 0
#> 19 ape Absolute Percentage Error (per observation) regr 0
#> 20 mauc_aunu Average 1 vs. rest multiclass AUC classif 0
#> 21 pinball Pinball regr -Inf
#> 22 fnr False Negative Rate binary 0
#> 23 tp True Positives binary 0
#> 24 srho Spearman's rho regr -1
#> 25 ae Absolute Error (per observation) regr 0
#> 26 rsq R Squared regr -Inf
#> 27 jaccard Jaccard Similarity Index similarity 0
#> 28 npv Negative Predictive Value binary 0
#> 29 sle Squared Log Error (per observation) regr 0
#> 30 dor Diagnostic Odds Ratio binary 0
#> 31 bacc Balanced Accuracy classif 0
#> 32 ppv Positive Predictive Value binary 0
#> 33 medse Median Squared Error regr 0
#> 34 auc Area Under the ROC Curve binary 0
#> 35 rae Relative Absolute Error regr 0
#> 36 sae Sum of Absolute Errors regr 0
#> 37 rmse Root Mean Squared Error regr 0
#> 38 ktau Kendall's tau regr -1
#> 39 tpr True Positive Rate binary 0
#> 40 mcc Matthews Correlation Coefficient classif -1
#> 41 linex Linear-exponential Loss (per observation) regr 0
#> 42 fn False Negatives binary 0
#> 43 maxse Max Squared Error regr 0
#> 44 gmean Geometric Mean of Recall and Specificity binary 0
#> 45 fp False Positives binary 0
#> 46 bias Bias regr -Inf
#> 47 fdr False Discovery Rate binary 0
#> 48 prauc Precision-Recall Curve binary 0
#> 49 logloss Log Loss classif 0
#> 50 mauc_mu Multiclass mu AUC classif 0
#> 51 mse Mean Squared Error regr 0
#> 52 fpr False Positive Rate binary 0
#> 53 mbrier Multiclass Brier Score classif 0
#> 54 ce Classification Error classif 0
#> 55 recall Recall binary 0
#> 56 gpr Geometric Mean of Precision and Recall binary 0
#> 57 specificity Specificity binary 0
#> 58 se Squared Error (per observation) regr 0
#> 59 rrse Root Relative Squared Error regr 0
#> 60 acc Classification Accuracy classif 0
#> 61 fbeta F-beta score binary 0
#> 62 medae Median Absolute Error regr 0
#> 63 sensitivity Sensitivity binary 0
#> 64 mape Mean Absolute Percent Error regr 0
#> 65 mauc_au1p Weighted average 1 vs. 1 multiclass AUC classif 0
#> upper minimize sample_weights
#> 1 Inf TRUE TRUE
#> 2 1 FALSE FALSE
#> 3 1 FALSE FALSE
#> 4 Inf TRUE FALSE
#> 5 1 TRUE TRUE
#> 6 Inf TRUE TRUE
#> 7 Inf TRUE FALSE
#> 8 1 TRUE FALSE
#> 9 1 FALSE FALSE
#> 10 Inf TRUE TRUE
#> 11 Inf NA TRUE
#> 12 1 FALSE FALSE
#> 13 1 FALSE FALSE
#> 14 2 TRUE FALSE
#> 15 1 TRUE FALSE
#> 16 Inf TRUE TRUE
#> 17 1 FALSE FALSE
#> 18 Inf FALSE FALSE
#> 19 Inf TRUE FALSE
#> 20 1 FALSE FALSE
#> 21 Inf TRUE TRUE
#> 22 1 TRUE FALSE
#> 23 Inf FALSE FALSE
#> 24 1 FALSE FALSE
#> 25 Inf TRUE FALSE
#> 26 1 FALSE FALSE
#> 27 1 FALSE FALSE
#> 28 1 FALSE FALSE
#> 29 Inf TRUE FALSE
#> 30 Inf FALSE FALSE
#> 31 1 FALSE TRUE
#> 32 1 FALSE FALSE
#> 33 Inf TRUE FALSE
#> 34 1 FALSE FALSE
#> 35 Inf TRUE FALSE
#> 36 Inf TRUE TRUE
#> 37 Inf TRUE TRUE
#> 38 1 FALSE FALSE
#> 39 1 FALSE FALSE
#> 40 1 FALSE FALSE
#> 41 Inf TRUE FALSE
#> 42 Inf TRUE FALSE
#> 43 Inf TRUE FALSE
#> 44 1 FALSE FALSE
#> 45 Inf TRUE FALSE
#> 46 Inf NA TRUE
#> 47 1 TRUE FALSE
#> 48 1 FALSE FALSE
#> 49 Inf TRUE TRUE
#> 50 1 FALSE FALSE
#> 51 Inf TRUE TRUE
#> 52 1 TRUE FALSE
#> 53 2 TRUE FALSE
#> 54 1 TRUE TRUE
#> 55 1 FALSE FALSE
#> 56 1 FALSE FALSE
#> 57 1 FALSE FALSE
#> 58 Inf TRUE FALSE
#> 59 Inf TRUE FALSE
#> 60 1 FALSE TRUE
#> 61 1 FALSE FALSE
#> 62 Inf TRUE FALSE
#> 63 1 FALSE FALSE
#> 64 Inf TRUE TRUE
#> 65 1 FALSE FALSE