Performance Measures for 'mlr3'
Classification Accuracy
Absolute Error (per observation)
Absolute Percentage Error (per observation)
Area Under the ROC Curve
Balanced Accuracy
Binary Brier Score
Bias
Binary Classification Parameters
Classification Error
Classification Parameters
Calculate Binary Confusion Matrix
Diagnostic Odds Ratio
F-beta Score
False Discovery Rate
False Negatives
False Negative Rate
False Omission Rate
False Positives
False Positive Rate
Geometric Mean of Recall and Specificity
Geometric Mean of Precision and Recall
Jaccard Similarity Index
Kendall's tau
Linear-Exponential Loss (per observation)
Log Loss
Mean Absolute Error
Mean Absolute Percent Error
Multiclass AUC Scores
Max Absolute Error
Max Squared Error
Multiclass Brier Score
Matthews Correlation Coefficient
Measure Registry
Median Absolute Error
Median Squared Error
mlr3measures: Performance Measures for 'mlr3'
Mean Squared Error
Mean Squared Log Error
Negative Predictive Value
Percent Bias
Phi Coefficient Similarity
Average Pinball Loss
Positive Predictive Value
Area Under the Precision-Recall Curve
Relative Absolute Error
Regression Parameters
Root Mean Squared Error
Root Mean Squared Log Error
Root Relative Squared Error
Relative Squared Error
R Squared
Sum of Absolute Errors
Squared Error (per observation)
Similarity Parameters
Squared Log Error (per observation)
Symmetric Mean Absolute Percent Error
Spearman's rho
Sum of Squared Errors
True Negatives
True Negative Rate
True Positives
True Positive Rate
Zero-One Classification Loss (per observation)
Implements multiple performance measures for supervised learning. Includes over 40 measures for regression and classification. Additionally, meta information about the performance measures can be queried, e.g. what the best and worst possible performances scores are.
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