Evaluation Metrics for Machine Learning

---

Documentation for package ‘Metrics’ version 0.1.4

• DESCRIPTION file.

Help Pages

accuracy	Accuracy
ae	Absolute Error
ape	Absolute Percent Error
apk	Average Precision at k
auc	Area under the ROC curve (AUC)
bias	Bias
ce	Classification Error
f1	F1 Score
fbeta_score	F-beta Score
ll	Log Loss
logLoss	Mean Log Loss
mae	Mean Absolute Error
mape	Mean Absolute Percent Error
mapk	Mean Average Precision at k
mase	Mean Absolute Scaled Error
mdae	Median Absolute Error
MeanQuadraticWeightedKappa	Mean Quadratic Weighted Kappa
mse	Mean Squared Error
msle	Mean Squared Log Error
params_binary	Inherit Documentation for Binary Classification Metrics
params_classification	Inherit Documentation for Classification Metrics
params_regression	Inherit Documentation for Regression Metrics
percent_bias	Percent Bias
precision	Precision
rae	Relative Absolute Error
recall	Recall
rmse	Root Mean Squared Error
rmsle	Root Mean Squared Log Error
rrse	Root Relative Squared Error
rse	Relative Squared Error
ScoreQuadraticWeightedKappa	Quadratic Weighted Kappa
se	Squared Error
sle	Squared Log Error
smape	Symmetric Mean Absolute Percentage Error
sse	Sum of Squared Errors