A simple mlr3misc::Dictionary storing objects of class Measure.
Each measure has an associated help page, see mlr_measures_[id].
This dictionary can get populated with additional measures by add-on packages. E.g., mlr3proba adds survival measures and mlr3cluster adds cluster analysis measures.
For a more convenient way to retrieve and construct measures, see msr()/msrs().
Format
R6::R6Class object inheriting from mlr3misc::Dictionary.
Methods
See mlr3misc::Dictionary.
S3 methods
as.data.table(dict, ..., objects = FALSE)
mlr3misc::Dictionary ->data.table::data.table()
Returns adata.table::data.table()with fields "key", "label", "task_type", "packages", "predict_type", and "task_properties" as columns. Ifobjectsis set toTRUE, the constructed objects are returned in the list column namedobject.
See also
Sugar functions: msr(), msrs()
Implementation of most measures: mlr3measures
Other Dictionary:
mlr_learners,
mlr_resamplings,
mlr_task_generators,
mlr_tasks
Other Measure:
Measure,
MeasureClassif,
MeasureRegr,
MeasureSimilarity,
mlr_measures_aic,
mlr_measures_bic,
mlr_measures_classif.costs,
mlr_measures_debug_classif,
mlr_measures_elapsed_time,
mlr_measures_internal_valid_score,
mlr_measures_oob_error,
mlr_measures_regr.pinball,
mlr_measures_regr.rsq,
mlr_measures_selected_features
Examples
as.data.table(mlr_measures)
#> Key: <key>
#> key label
#> <char> <char>
#> 1: aic Akaike Information Criterion
#> 2: bic Bayesian Information Criterion
#> 3: classif.acc Classification Accuracy
#> 4: classif.auc Area Under the ROC Curve
#> 5: classif.bacc Balanced Accuracy
#> 6: classif.bbrier Binary Brier Score
#> 7: classif.ce Classification Error
#> 8: classif.costs Cost-sensitive Classification
#> 9: classif.dor Diagnostic Odds Ratio
#> 10: classif.fbeta F-beta score
#> 11: classif.fdr False Discovery Rate
#> 12: classif.fn False Negatives
#> 13: classif.fnr False Negative Rate
#> 14: classif.fomr False Omission Rate
#> 15: classif.fp False Positives
#> 16: classif.fpr False Positive Rate
#> 17: classif.logloss Log Loss
#> 18: classif.mauc_au1p Weighted average 1 vs. 1 multiclass AUC
#> 19: classif.mauc_au1u Average 1 vs. 1 multiclass AUC
#> 20: classif.mauc_aunp Weighted average 1 vs. rest multiclass AUC
#> 21: classif.mauc_aunu Average 1 vs. rest multiclass AUC
#> 22: classif.mauc_mu Multiclass mu AUC
#> 23: classif.mbrier Multiclass Brier Score
#> 24: classif.mcc Matthews Correlation Coefficient
#> 25: classif.npv Negative Predictive Value
#> 26: classif.ppv Positive Predictive Value
#> 27: classif.prauc Precision-Recall Curve
#> 28: classif.precision Precision
#> 29: classif.recall Recall
#> 30: classif.sensitivity Sensitivity
#> 31: classif.specificity Specificity
#> 32: classif.tn True Negatives
#> 33: classif.tnr True Negative Rate
#> 34: classif.tp True Positives
#> 35: classif.tpr True Positive Rate
#> 36: debug_classif Debug Classification Measure
#> 37: internal_valid_score Internal Validation Score
#> 38: oob_error Out-of-bag Error
#> 39: regr.bias Bias
#> 40: regr.ktau Kendall's tau
#> 41: regr.mae Mean Absolute Error
#> 42: regr.mape Mean Absolute Percent Error
#> 43: regr.maxae Max Absolute Error
#> 44: regr.medae Median Absolute Error
#> 45: regr.medse Median Squared Error
#> 46: regr.mse Mean Squared Error
#> 47: regr.msle Mean Squared Log Error
#> 48: regr.pbias Percent Bias
#> 49: regr.pinball <NA>
#> 50: regr.rmse Root Mean Squared Error
#> 51: regr.rmsle Root Mean Squared Log Error
#> 52: regr.rsq <NA>
#> 53: regr.sae Sum of Absolute Errors
#> 54: regr.smape Symmetric Mean Absolute Percent Error
#> 55: regr.srho Spearman's rho
#> 56: regr.sse Sum of Squared Errors
#> 57: selected_features Absolute or Relative Frequency of Selected Features
#> 58: sim.jaccard Jaccard Similarity Index
#> 59: sim.phi Phi Coefficient Similarity
#> 60: time_both Elapsed Time
#> 61: time_predict Elapsed Time
#> 62: time_train Elapsed Time
#> key label
#> task_type packages predict_type
#> <char> <list> <char>
#> 1: <NA> mlr3 <NA>
#> 2: <NA> mlr3 <NA>
#> 3: classif mlr3,mlr3measures response
#> 4: classif mlr3,mlr3measures prob
#> 5: classif mlr3,mlr3measures response
#> 6: classif mlr3,mlr3measures prob
#> 7: classif mlr3,mlr3measures response
#> 8: classif mlr3 response
#> 9: classif mlr3,mlr3measures response
#> 10: classif mlr3,mlr3measures response
#> 11: classif mlr3,mlr3measures response
#> 12: classif mlr3,mlr3measures response
#> 13: classif mlr3,mlr3measures response
#> 14: classif mlr3,mlr3measures response
#> 15: classif mlr3,mlr3measures response
#> 16: classif mlr3,mlr3measures response
#> 17: classif mlr3,mlr3measures prob
#> 18: classif mlr3,mlr3measures prob
#> 19: classif mlr3,mlr3measures prob
#> 20: classif mlr3,mlr3measures prob
#> 21: classif mlr3,mlr3measures prob
#> 22: classif mlr3,mlr3measures prob
#> 23: classif mlr3,mlr3measures prob
#> 24: classif mlr3,mlr3measures response
#> 25: classif mlr3,mlr3measures response
#> 26: classif mlr3,mlr3measures response
#> 27: classif mlr3,mlr3measures prob
#> 28: classif mlr3,mlr3measures response
#> 29: classif mlr3,mlr3measures response
#> 30: classif mlr3,mlr3measures response
#> 31: classif mlr3,mlr3measures response
#> 32: classif mlr3,mlr3measures response
#> 33: classif mlr3,mlr3measures response
#> 34: classif mlr3,mlr3measures response
#> 35: classif mlr3,mlr3measures response
#> 36: <NA> mlr3 response
#> 37: <NA> mlr3 <NA>
#> 38: <NA> mlr3 <NA>
#> 39: regr mlr3,mlr3measures response
#> 40: regr mlr3,mlr3measures response
#> 41: regr mlr3,mlr3measures response
#> 42: regr mlr3,mlr3measures response
#> 43: regr mlr3,mlr3measures response
#> 44: regr mlr3,mlr3measures response
#> 45: regr mlr3,mlr3measures response
#> 46: regr mlr3,mlr3measures response
#> 47: regr mlr3,mlr3measures response
#> 48: regr mlr3,mlr3measures response
#> 49: regr mlr3 quantiles
#> 50: regr mlr3,mlr3measures response
#> 51: regr mlr3,mlr3measures response
#> 52: regr mlr3 response
#> 53: regr mlr3,mlr3measures response
#> 54: regr mlr3,mlr3measures response
#> 55: regr mlr3,mlr3measures response
#> 56: regr mlr3,mlr3measures response
#> 57: <NA> mlr3 <NA>
#> 58: <NA> mlr3,mlr3measures <NA>
#> 59: <NA> mlr3,mlr3measures <NA>
#> 60: <NA> mlr3 <NA>
#> 61: <NA> mlr3 <NA>
#> 62: <NA> mlr3 <NA>
#> task_type packages predict_type
#> properties
#> <list>
#> 1: na_score,requires_learner,requires_model,requires_no_prediction
#> 2: na_score,requires_learner,requires_model,requires_no_prediction
#> 3: weights
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#> 5: weights
#> 6: weights
#> 7: weights
#> 8: weights
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#> 36: na_score
#> 37: na_score,requires_learner,requires_no_prediction
#> 38: na_score,requires_learner,requires_no_prediction
#> 39: weights
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#> 41: weights
#> 42: weights
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#> 47: weights
#> 48: weights
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#> 51: weights
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#> 57: requires_task,requires_learner,requires_model,requires_no_prediction
#> 58: requires_model,requires_no_prediction
#> 59: requires_model,requires_no_prediction
#> 60: requires_learner,requires_no_prediction
#> 61: requires_learner,requires_no_prediction
#> 62: requires_learner,requires_no_prediction
#> properties
#> task_properties
#> <list>
#> 1:
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#> 4: twoclass
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#> 6: twoclass
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#> 9: twoclass
#> 10: twoclass
#> 11: twoclass
#> 12: twoclass
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#> 25: twoclass
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#> 33: twoclass
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#> task_properties
mlr_measures$get("classif.ce")
#>
#> ── <MeasureClassifSimple> (classif.ce): Classification Error ───────────────────
#> • Packages: mlr3 and mlr3measures
#> • Range: [0, 1]
#> • Minimize: TRUE
#> • Average: macro
#> • Parameters: list()
#> • Properties: weights
#> • Predict type: response
#> • Predict sets: test
#> • Aggregator: mean()
msr("regr.mse")
#>
#> ── <MeasureRegrSimple> (regr.mse): Mean Squared Error ──────────────────────────
#> • Packages: mlr3 and mlr3measures
#> • Range: [0, Inf]
#> • Minimize: TRUE
#> • Average: macro
#> • Parameters: list()
#> • Properties: weights
#> • Predict type: response
#> • Predict sets: test
#> • Aggregator: mean()