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Measures

Measures operate on Prediction objects generated by learners. They quantify the prediction by comparing prediction with ground truth. The Measure objects provide an abstraction for a plethora of performance measures.

Superclass MeasureClust has cloneable=FALSE, but subclass MeasureClustFPC has cloneable=TRUE. A subclass cannot be cloneable when its superclass is not cloneable, so cloning will be disabled for MeasureClustFPC.
Superclass MeasureClust has cloneable=FALSE, but subclass MeasureClustFPC has cloneable=TRUE. A subclass cannot be cloneable when its superclass is not cloneable, so cloning will be disabled for MeasureClustFPC.
Superclass MeasureClust has cloneable=FALSE, but subclass MeasureClustSil has cloneable=TRUE. A subclass cannot be cloneable when its superclass is not cloneable, so cloning will be disabled for MeasureClustSil.
Superclass MeasureClust has cloneable=FALSE, but subclass MeasureClustFPC has cloneable=TRUE. A subclass cannot be cloneable when its superclass is not cloneable, so cloning will be disabled for MeasureClustFPC.
Key
Label
Task Type
Packages
Akaike Information Criterion
generic
Bayesian Information Criterion
generic
Default CI
generic
Conservative-Z Interval
generic
Corrected-T Interval
generic
Holdout Interval
generic
Nested CV Interval
generic
Wald CV Interval
generic
Classification Accuracy
classif
Area Under the ROC Curve
classif
Balanced Accuracy
classif
Binary Brier Score
classif
Classification Error
classif
Cost-sensitive Classification
classif
Diagnostic Odds Ratio
classif
F-beta score
classif
False Discovery Rate
classif
False Negatives
classif
False Negative Rate
classif
False Omission Rate
classif
False Positives
classif
False Positive Rate
classif
Log Loss
classif
Weighted average 1 vs. 1 multiclass AUC
classif
Average 1 vs. 1 multiclass AUC
classif
Weighted average 1 vs. rest multiclass AUC
classif
Average 1 vs. rest multiclass AUC
classif
Multiclass mu AUC
classif
Multiclass Brier Score
classif
Matthews Correlation Coefficient
classif
Negative Predictive Value
classif
Positive Predictive Value
classif
Precision-Recall Curve
classif
Precision
classif
Recall
classif
Sensitivity
classif
Specificity
classif
True Negatives
classif
True Negative Rate
classif
True Positives
classif
True Positive Rate
classif
Calinski Harabasz
clust
Dunn
clust
Silhouette
clust
Within Sum of Squares
clust
Debug Classification Measure
generic
Internal Validation Score
generic
Out-of-bag Error
generic
Bias
regr
Kendall's tau
regr
Mean Absolute Error
regr
Mean Absolute Percent Error
regr
Max Absolute Error
regr
Median Absolute Error
regr
Median Squared Error
regr
Mean Squared Error
regr
Mean Squared Log Error
regr
Percent Bias
regr
regr
Root Mean Squared Error
regr
Root Mean Squared Log Error
regr
regr
regr
Sum of Absolute Errors
regr
Symmetric Mean Absolute Percent Error
regr
Spearman's rho
regr
Sum of Squared Errors
regr
Absolute or Relative Frequency of Selected Features
generic
Jaccard Similarity Index
generic
Phi Coefficient Similarity
generic
Elapsed Time
generic
Elapsed Time
generic
Elapsed Time
generic