Provides a concise, user-friendly summary of model performances. For classification: - Shows Accuracy, F1 Score, Kappa, Precision, ROC AUC, Sensitivity, Specificity. - Produces a bar plot of these metrics. - Shows ROC curves for binary classification using yardstick::roc_curve(). - Displays a confusion matrix and a calibration plot if probabilities are available.
# S3 method for fastml_model
summary(
object,
algorithm = "best",
sort_metric = NULL,
plot = TRUE,
notes = "",
...
)Prints summary and plots if requested.
An object of class fastml_model.
A vector of algorithm names to display summary. Default is "best".
The metric to sort by. Default uses optimized metric.
Logical. If TRUE, produce bar plot, yardstick-based ROC curves (for binary classification), confusion matrix (classification), smooth calibration plot (if probabilities), and residual plots (regression).
User-defined commentary.
Additional arguments.
For regression: - Shows RMSE, R-squared, and MAE. - Produces a bar plot of these metrics. - Displays residual diagnostics (truth vs predicted, residual distribution).