MLMetrics: Collection of Machine Learning Model Metrics for Easy...
In JobNmadu/Dyn4cast: Dynamic Modeling and Machine Learning Environment
MLMetrics R Documentation
Collection of Machine Learning Model Metrics for Easy Reference
Description
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, Model, K, Name, Form, kutuf, TTy)
Arguments
Observed
The Observed data in a data frame format
yvalue
The Response variable of the estimated Model
Model
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.
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)
Model <- lm(states ~ bs(sequence, knots = c(30, 115)), data = Data)
MLMetrics(Observed = Data, yvalue = Data$states, Model = Model, K = 2,
Name = "Linear", Form = "LM", kutuf = 0, TTy = "Number")
JobNmadu/Dyn4cast documentation built on March 5, 2025, 9:56 p.m.
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| MLMetrics | R Documentation |
Collection of Machine Learning Model Metrics for Easy Reference
Description
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, Model, K, Name, Form, kutuf, TTy)
Arguments
Observed |
The Observed data in a data frame format |
yvalue |
The Response variable of the estimated Model |
Model |
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. |
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)
Model <- lm(states ~ bs(sequence, knots = c(30, 115)), data = Data)
MLMetrics(Observed = Data, yvalue = Data$states, Model = Model, K = 2,
Name = "Linear", Form = "LM", kutuf = 0, TTy = "Number")
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