Integrating {SLmetrics} with other pkgs
Documentation
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Preface
1
Introduction
2
Summary
3
Benchmarking
Regression functions
Concordance Correlation Coefficient
Huber Loss Function
Mean Absolute Error
Mean Absolute Percentage error
Matthews Correlation Coefficient
Mean Squared Error
Pinball Loss
Relative Absolute Error
Root Mean Squared Error
Root Mean Squared Logarithmic Error
Relative Root Mean Squared Error
Root Relative Squared Error
Symmetric Mean Absolute Percentage Error
Classification functions
Receiver Operator Characteristics
Accuracy
Balanced Accuracy
Cohen’s Kappa Statistic
Confusion Matrix
Diagnostic Odds Ratio
Entropy
F-beta Score
False Discovery Rate
False Omission Rate
Fowlkes-Mallows Index
False Positive Rate
Jaccard Score
Log Loss
Mean Percentage Error
Negative Likelihood Ratio
Negative Predictive Value
Positive Likelihood Ratio
Area under the Precision-Recall Curve
Precision-Recall Curve
Precision
Recall
Area under the Receiver Operator Characteristics Curve
Coefficient of Determination
Specificity
Zero-One Loss
Integrating {SLmetrics} with other pkgs
Using {lightgbm} and {SLmetrics} in classification tasks
Using {xgboost} and {SLmetrics} in regression tasks
4
OpenMP
5
Garbage in, garbage out
References
Integrating {SLmetrics} with other pkgs
In this section a few examples on how to integrate
{SLmetrics}
into other
R
packages is given.
Zero-One Loss
Using {lightgbm} and {SLmetrics} in classification tasks