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Metrics

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 (35)

MeanQuadraticWeightedKappa

Mean Quadratic Weighted Kappa

ScoreQuadraticWeightedKappa

Quadratic Weighted Kappa

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

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

se

Squared Error

sle

Squared Log Error

smape

Symmetric Mean Absolute Percentage Error

sse

Sum of Squared Errors

Metrics

Evaluation Metrics for Machine Learning

v0.1.4
BSD_3_clause + file LICENSE
Authors
Ben Hamner [aut, cph], Michael Frasco [aut, cre], Erin LeDell [ctb]
Initial release

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