Metrics v0.1.4


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Evaluation Metrics for Machine Learning

An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.

Functions in Metrics

Name Description
mdae Median Absolute Error
ll Log Loss
params_binary Inherit Documentation for Binary Classification Metrics
rae Relative Absolute Error
precision Precision
fbeta_score F-beta Score
mse Mean Squared Error
params_classification Inherit Documentation for Classification Metrics
f1 F1 Score
recall Recall
msle Mean Squared Log Error
rmsle Root Mean Squared Log Error
rrse Root Relative Squared Error
sle Squared Log Error
smape Symmetric Mean Absolute Percentage Error
rmse Root Mean Squared Error
rse Relative Squared Error
se Squared Error
ce Classification Error
ae Absolute Error
ScoreQuadraticWeightedKappa Quadratic Weighted Kappa
accuracy Accuracy
auc Area under the ROC curve (AUC)
MeanQuadraticWeightedKappa Mean Quadratic Weighted Kappa
apk Average Precision at k
ape Absolute Percent Error
mase Mean Absolute Scaled Error
bias Bias
logLoss Mean Log Loss
mae Mean Absolute Error
mape Mean Absolute Percent Error
params_regression Inherit Documentation for Regression Metrics
percent_bias Percent Bias
mapk Mean Average Precision at k
sse Sum of Squared Errors
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License BSD_3_clause + file LICENSE
RoxygenNote 6.0.1
NeedsCompilation no
Packaged 2018-07-09 03:33:50 UTC; mfrasco
Repository CRAN
Date/Publication 2018-07-09 04:30:18 UTC
suggests testthat
Contributors Ben Hamner, Erin LeDell

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