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Metrics (version 0.1.3)

Evaluation Metrics for Machine Learning

Description

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.

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Install

install.packages('Metrics')

Monthly Downloads

26,208

Version

0.1.3

License

BSD_3_clause + file LICENSE

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Maintainer

Michael Frasco

Last Published

November 3rd, 2017

Functions in Metrics (0.1.3)

msle

Mean Squared Log Error
params_binary

Inherit Documentation for Binary Classification Metrics
percent_bias

Percent Bias
rae

Relative Absolute Error
f1

F1 Score
ce

Classification Error
mapk

Mean Average Precision at k
mase

Mean Absolute Scaled Error
MeanQuadraticWeightedKappa

Mean Quadratic Weighted Kappa
mdae

Median Absolute Error
ScoreQuadraticWeightedKappa

Quadratic Weighted Kappa
mse

Mean Squared Error
rmse

Root Mean Squared Error
accuracy

Accuracy
auc

Area under the ROC curve (AUC)
rmsle

Root Mean Squared Log Error
ae

Absolute Error
bias

Bias
mae

Mean Absolute Error
params_classification

Inherit Documentation for Classification Metrics
mape

Mean Absolute Percent Error
rrse

Root Relative Squared Error
params_regression

Inherit Documentation for Regression Metrics
rse

Relative Squared Error
ape

Absolute Percent Error
apk

Average Precision at k
ll

Log Loss
se

Squared Error
smape

Symmetric Mean Absolute Percentage Error
logLoss

Mean Log Loss
sse

Sum of Squared Errors
sle

Squared Log Error