Description Usage Arguments Value See Also Examples
This function calculates evaluation metrics for classifications / predictions and their corresponding observed values. The function calculates 9 different classification metrics; Accuaracy, True Poistive Rate, False Positive Rate, True Negative Rate, False Negative Rate, Positive Prediction Rate, Negative Predicition Rate and F1 Score. The function calculates 7 different prediction metrics; Sum Squared Error, Sum Absolute Error, Mean Sum Squared Error, Mean Absolute Error, Root Mean Squared Error, Root Mean Absolute Error and R Squared The metrics are outputted as a data frame.
1 2 |
y_obs |
The true observations. |
y_pred |
The model predictions. |
plots |
Logical, indeicating whether to return appropriate plots. |
file_name |
A character object indicating the file name when saving the data frame. The default is NULL. The name must include the .csv suffixs. |
directory |
A character object specifying the directory where the data frame is to be saved as a .csv file. |
Outputs the metrics as a data frame
ensemble_mars, ensemble_glmnet
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | #-- Classification Example --#
y_obs = as.factor(c(1,1,0,1,0,1,1,1,0,0,1,1,0,1,0,0,0,1,0,0,0))
y_pred = as.factor(c(1,1,0,1,0,1,1,0,0,1,1,0,0,1,0,1,1,0,0,0,1))
evaluation_metrics(y_obs, y_pred, type = 'classification')
y_obs = y_pred
evaluation_metrics(y_obs, y_pred, type = 'classification')
#-- Prediction Example --#
y_obs = rnorm(n = 150, sd = 10, mean = 75)
y_pred = rnorm(n = 150, sd = 25, mean = 75)
evaluation_metrics(y_obs, y_pred, type = 'prediction', plots = TRUE)
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