# 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 No Results!