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Metrics (version 0.1.4)

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

27,287

Version

0.1.4

License

BSD_3_clause + file LICENSE

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Last Published

July 9th, 2018

Functions in Metrics (0.1.4)

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