R: Evaluation Metrics for Machine Learning

# 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