plot_robustness: Plot Robustness Decay Curve

In TrustworthyMLR: Stability and Robustness Evaluation for Machine Learning Models

Plot Robustness Decay Curve

Description

Visualizes how model performance (robustness) decreases as the level of input noise increases. This "decay curve" is a powerful tool for understanding the sensitivity threshold of a machine learning model.

Usage

plot_robustness(
  predict_fn,
  X,
  levels = seq(0, 0.3, by = 0.05),
  n_rep = 5L,
  ...
)

Arguments

predict_fn	A function that accepts a numeric matrix and returns a numeric vector of predictions.
X	A numeric matrix or data.frame of input features.
levels	A numeric vector of noise levels to evaluate. Default is seq(0, 0.3, by = 0.05).
n_rep	Number of repetitions for each noise level. Default is 5L.
...	Additional arguments passed to plot.

Value

A data.frame with columns noise_level and robustness_score.

Examples

# Simple model
pred_fn <- function(X) X %*% c(1, -1)
X <- matrix(rnorm(200), ncol = 2)

# Plot decay
plot_robustness(pred_fn, X, main = "Model Robustness Decay")

TrustworthyMLR documentation built on Feb. 20, 2026, 5:09 p.m.