Tracking issue: List of measures
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- Dominant language
- Julia
- Stars
- 163
- Forks
- 46
- Avg merge
- 1d 18h
- Merged PRs (30d)
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Description
https://alan-turing-institute.github.io/MLJ.jl/dev/performance_measures/#List-of-measures
As of MLJBase 0.18.17
┌─────────────────────────────────────┬──────────────────────────────────────────────┐
│ name │ human_name │
│ String │ String │
│ Textual │ Textual │
├─────────────────────────────────────┼──────────────────────────────────────────────┤
│ Accuracy │ accuracy │
│ AreaUnderCurve │ area under the ROC │
│ BalancedAccuracy │ balanced accuracy │
│ BrierLoss │ Brier loss (a.k.a. quadratic loss) │
│ BrierScore │ Brier score (a.k.a. quadratic score) │
│ ConfusionMatrix │ confusion matrix │
│ FScore │ F-Score │
│ FalseDiscoveryRate │ false discovery rate │
│ FalseNegative │ number of false negatives │
│ FalseNegativeRate │ false negative rate │
│ FalsePositive │ number of false positives │
│ FalsePositiveRate │ false positive rate │
│ LPLoss │ lp loss │
│ LogCoshLoss │ log cosh loss │
│ LogLoss │ log loss │
│ MatthewsCorrelation │ matthews correlation │
│ MeanAbsoluteError │ mean absolute error │
│ MeanAbsoluteProportionalError │ mean absolute proportional error │
│ MisclassificationRate │ misclassification rate │
│ MulticlassFScore │ multiclass f score │
│ MulticlassFalseDiscoveryRate │ multiclass false discovery rate │
│ MulticlassFalseNegative │ multiclass false negative │
│ MulticlassFalseNegativeRate │ multiclass false negative rate │
│ MulticlassFalsePositive │ multiclass false positive │
│ MulticlassFalsePositiveRate │ multiclass false positive rate │
│ MulticlassNegativePredictiveValue │ multiclass negative predictive value │
│ MulticlassPrecision │ multiclass precision │
│ MulticlassTrueNegative │ multiclass true negative │
│ MulticlassTrueNegativeRate │ multiclass true negative rate │
│ MulticlassTruePositive │ multiclass true positive │
│ MulticlassTruePositiveRate │ multiclass true positive rate │
│ NegativePredictiveValue │ negative predictive value │
│ Precision │ precision (a.k.a. positive predictive value) │
│ RootMeanSquaredError │ root mean squared error │
│ RootMeanSquaredLogError │ root mean squared log error │
│ RootMeanSquaredLogProportionalError │ root mean squared log proportional error │
│ RootMeanSquaredProportionalError │ root mean squared proportional error │
│ TrueNegative │ number of true negatives │
│ TrueNegativeRate │ true negative rate │
│ TruePositive │ number of true positives │
│ TruePositiveRate │ true positive rate (a.k.a recall) │
│ DWDMarginLoss │ distance weighted discrimination loss │
│ ExpLoss │ exp loss │
│ L1HingeLoss │ l1 hinge loss │
│ L2HingeLoss │ l2 hinge loss │
│ L2MarginLoss │ l2 margin loss │
│ LogitMarginLoss │ logit margin loss │
│ ModifiedHuberLoss │ modified huber loss │
│ PerceptronLoss │ perceptron loss │
│ SigmoidLoss │ sigmoid loss │
│ SmoothedL1HingeLoss │ smoothed l1 hinge loss │
│ ZeroOneLoss │ zero one loss │
│ HuberLoss │ huber loss │
│ L1EpsilonInsLoss │ l1 ϵ-insensitive loss │
│ L2EpsilonInsLoss │ l2 ϵ-insensitive loss │
│ LPDistLoss │ lp dist loss │
│ LogitDistLoss │ logit dist loss │
│ PeriodicLoss │ periodic loss │
│ QuantileLoss │ quantile loss │
└─────────────────────────────────────┴──────────────────────────────────────────────┘
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the linked performance-measures documentation page and compare it with the pasted MLJBase 0.18.17 list. Determine which measures or names need updating; done means the documentation list matches the intended MLJBase measure set.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100