Skip to contents

Collection of Machine Learning Model Metrics for Easy Reference

Source: R/MLMetrics.R
MLMetrics.Rd

This function estimates over 40 Metrics for assessing the quality of Machine Learning Models. The purpose is to provide a wrapper which brings all the metrics on the table and makes it easier to use them to select a model.

Usage

MLMetrics(Observed, yvalue, modeli, K, Name, Form, kutuf, TTy)

Arguments

Observed

The Observed data in a data frame format

yvalue

The Response variable of the estimated Model

modeli

The Estimated Model (Model = a + bx)

K

The number of variables in the estimated Model to consider

Name

The Name of the Models that need to be specified. They are ARIMA, Values if the model computes the fitted value without estimation like Essembles, SMOOTH (smooth.spline), Logit, Ensembles based on weight - EssemWet, QUADRATIC polynomial, SPLINE polynomial.

Form

Form of the Model Estimated (LM, ALM, GLM, N-LM, ARDL)

kutuf

Cutoff for the Estimated values (defaults to 0.5 if not specified)

TTy

Type of response variable (Numeric or Response - like binary)

Value

A list with the following components:

Absolute Error

of the Model.

Absolute Percent Error

of the Model.

Accuracy

of the Model.

Adjusted R Square

of the Model.

`Akaike's` Information Criterion AIC

of the Model.

Area under the ROC curve (AUC)

of the Model.

Average Precision at k

of the Model.

Bias

of the Model.

Brier score

of the Model.

Classification Error

of the Model.

F1 Score

of the Model.

fScore

of the Model.

GINI Coefficient

of the Model.

kappa statistic

of the Model.

Log Loss

of the Model.

`Mallow's` cp

of the Model.

Matthews Correlation Coefficient

of the Model.

Mean Log Loss

of the Model.

Mean Absolute Error

of the Model.

Mean Absolute Percent Error

of the Model.

Mean Average Precision at k

of the Model.

Mean Absolute Scaled Error

of the Model.

Median Absolute Error

of the Model.

Mean Squared Error

of the Model.

Mean Squared Log Error

of the Model.

Model turning point error

of the Model.

Negative Predictive Value

of the Model.

Percent Bias

of the Model.

Positive Predictive Value

of the Model.

Precision

of the Model.

Predictive Residual Sum of Squares

of the Model.

R Square

of the Model.

Relative Absolute Error

of the Model.

Recall

of the Model.

Root Mean Squared Error

of the Model.

Root Mean Squared Log Error

of the Model.

Root Relative Squared Error

of the Model.

Relative Squared Error

of the Model.

`Schwarz's` Bayesian criterion BIC

of the Model.

Sensitivity

of the Model.

specificity

of the Model.

Squared Error

of the Model.

Squared Log Error

of the Model.

Symmetric Mean Absolute Percentage Error

of the Model.

Sum of Squared Errors

of the Model.

True negative rate

of the Model.

True positive rate

of the Model.

Examples

library(splines)
library(readr)
Model   <- lm(states ~ bs(sequence, knots = c(30, 115)), data = Data)
MLMetrics(Observed = Data, yvalue = Data$states, modeli = Model, K = 2,
 Name = "Linear", Form = "LM", kutuf = 0, TTy = "Number")
#> Warning: NaNs produced
#> Warning: actual should be a list of vectors. Converting to a list.
#> Warning: predicted should be a list of vectors. Converting to a list.
#> $`Absolute Error`
#> [1] 460
#> 
#> $`Absolute Percent Error`
#> [1] 51
#> 
#> $Accuracy
#> [1] 0
#> 
#> $`Adjusted R Square`
#> [1] 0.77
#> 
#> $`Akaike's Information Criterion AIC`
#> [1] 1000
#> 
#> $`Area under the ROC curve (AUC)`
#> [1] 0
#> 
#> $`Average Precision at k`
#> [1] 0
#> 
#> $Bias
#> [1] 1.7e-16
#> 
#> $`Brier score`
#> [1] 8
#> 
#> $`Classification Error`
#> [1] 1
#> 
#> $`F1 Score`
#> [1] 0
#> 
#> $fScore
#> [1] 0
#> 
#> $`GINI Coefficient`
#> [1] 0.8
#> 
#> $`kappa statistic`
#> [1] 0
#> 
#> $`Log Loss`
#> [1] Inf
#> 
#> $`Mallow's cp`
#> [1] 3
#> 
#> $`Matthews Correlation Coefficient`
#> [1] 0
#> 
#> $`Mean Log Loss`
#> [1] -480
#> 
#> $`Mean Absolute Error`
#> [1] 2.3
#> 
#> $`Mean Absolute Percent Error`
#> [1] 0.25
#> 
#> $`Mean Average Precision at k`
#> [1] 0
#> 
#> $`Mean Absolute Scaled Error`
#> [1] 0.74
#> 
#> $`Median Absolute Error`
#> [1] 1.9
#> 
#> $`Mean Squared Error`
#> [1] 8.4
#> 
#> $`Mean Squared Log Error`
#> [1] 0.072
#> 
#> $`Model turning point error`
#> [1] 110
#> 
#> $`Negative Predictive Value`
#> [1] 0
#> 
#> $`Percent Bias`
#> [1] -0.1
#> 
#> $`Positive Predictive Value`
#> [1] 0
#> 
#> $Precision
#> [1] 1
#> 
#> $`Predictive Residual Sum of Squares`
#> [1] 0
#> 
#> $`R Square`
#> [1] 0.78
#> 
#> $`Relative Absolute Error`
#> [1] 0.47
#> 
#> $Recall
#> [1] 1
#> 
#> $`Root Mean Squared Error`
#> [1] 2.9
#> 
#> $`Root Mean Squared Log Error`
#> [1] 0.27
#> 
#> $`Root Relative Squared Error`
#> [1] 0.47
#> 
#> $`Relative Squared Error`
#> [1] 0.22
#> 
#> $`Schwarz's Bayesian criterion BIC`
#> [1] 1000
#> 
#> $Sensitivity
#> [1] 0
#> 
#> $specificity
#> [1] 0
#> 
#> $`Squared Error`
#> [1] 1700
#> 
#> $`Squared Log Error`
#> [1] 14
#> 
#> $`Symmetric Mean Absolute Percentage Error`
#> [1] 0.21
#> 
#> $`Sum of Squared Errors`
#> [1] 1700
#> 
#> $`True negative rate`
#> [1] 0
#> 
#> $`True positive rate`
#> [1] 0
#>